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Cited by 567Artificial Intelligence in Healthcare and EducationPrivacy-Preserving Technologies in DataEthics and Social Impacts of AI

Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare

Open access
Williamson, Steven M., Prybutok, Victor·2024·Applied Sciences
DOI 10.3390/app14020675
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Abstract

Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift with substantial potential for enhancing patient care. This paper critically examines this integration, confronting significant ethical, legal, and technological challenges, particularly in patient privacy, decision-making autonomy, and data integrity. A structured exploration of these issues focuses on Differential Privacy as a critical method for preserving patient confidentiality in AI-driven healthcare systems. We analyze the balance between privacy preservation and the practical utility of healthcare data, emphasizing the effectiveness of encryption, Differential Privacy, and mixed-model approaches. The paper navigates the complex ethical and legal frameworks essential for AI integration in healthcare. We comprehensively examine patient rights and the nuances of informed consent, along with the challenges of harmonizing advanced technologies like blockchain with the General Data Protection Regulation (GDPR). The issue of algorithmic bias in healthcare is also explored, underscoring the urgent need for effective bias detection and mitigation strategies to build patient trust. The evolving roles of decentralized data sharing, regulatory frameworks, and patient agency are discussed in depth. Advocating for an interdisciplinary, multi-stakeholder approach and responsive governance, the paper aims to align healthcare AI with ethical principles, prioritize patient-centered outcomes, and steer AI towards responsible and equitable enhancements in patient care.

AI snapshot

Written from the paper’s full text.

TL;DR

This PRISMA-guided literature review examines privacy, systemic oversight, and patient perceptions in AI-driven healthcare, focusing on differential privacy, encryption, blockchain, federated learning, and regulatory/ethical frameworks. It argues that privacy-preserving technologies and adaptive governance are needed to balance AI innovation with patient rights, though real-world effectiveness and stakeholder perspectives remain understudied.

Key findings

  • The review screened 875 initial articles, removed 483 duplicates, manually reviewed 392 abstracts, excluded 300 as out of scope, fully reviewed 92, excluded 65, and included 27 articles.
  • Blockchain, Federated Learning, Homomorphic Encryption, Differential Privacy, and Secure Multi-party Computation are presented as a combined framework for protecting patient privacy and data integrity; Differential Privacy is described as “lossy,” trading privacy against prediction accuracy or data utility, with hybrid approaches recommended.
  • Blockchain’s immutability creates tensions with GDPR, especially Articles 16 and 17 on rectification and erasure, and with data minimization and purpose limitation; private/permissioned blockchains may align better with GDPR, and codes of conduct/certification are suggested.
  • Ethical and regulatory concerns include algorithmic bias, transparency/explainability, dynamic or recurrent informed consent, patient agency, and the need for interdisciplinary, multi-stakeholder governance as AI systems evolve.

Methods

  • The review followed PRISMA guidelines and used AI research tools including Elicit, SciSpace, and Mirrorthink, with Boolean keyword searches covering AI, healthcare, privacy, anonymization, transparency, disparities, and patient understanding.
  • Inclusion criteria were peer-reviewed articles published 2018–2023 addressing AI in healthcare, especially ethical, legal, privacy, data security, algorithmic bias, and regulatory dimensions; non-peer-reviewed articles, opinion pieces, editorials, non-English articles, and studies outside the date range were excluded.
  • Screening proceeded in three steps: AI-assisted gathering, duplicate removal, title/abstract screening, full-text review, and data extraction using a modified PRISMA template, yielding 27 included articles.

Limitations

  • The paper states that rapid AI advances may have produced developments postdating the reviewed studies; Homomorphic Encryption and Differential Privacy face computational overhead and compromised utility or accuracy; bias detection and mitigation need further innovation; real-world effectiveness of Privacy-Preserving Machine Learning in healthcare is understudied; patient and healthcare professional perspectives are lacking; standardized privacy/security evaluation frameworks are needed; vulnerabilities and threats are not extensively covered; and more interdisciplinary research is required.
  • Evident from the text: the review relies on AI-assisted search and screening tools and limits inclusion to English-language, peer-reviewed 2018–2023 articles, which may constrain coverage; it reports no empirical validation or meta-analysis of the included technologies.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/app14020675

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 385 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    27 articles were deemed suitable for review.

    “After this rigorous screening process, only 27 of the original 875 articles were deemed suitable for review using the PRISMA template, as shown in Figure 1.”
    Quote in the paperSubstantial claim
  • Findingcitation

    Both patients and healthcare providers frequently encounter difficulties in understanding the decision-making processes of AI systems.

    “Both patients and healthcare providers frequently encounter difficulties in understanding the decision-making processes of AI systems.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

    • cites [38] Bernal, J., and Mazo, C. (2022). Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci., 12. source

      Not checked in the free run.

  • Findingnone given

    Key technologies such as Homomorphic Encryption and Differential Privacy face limitations like computational overhead and compromised utility or accuracy.

    “Despite their potential, key technologies, such as Homomorphic Encryption and Differential Privacy, face limitations like computational overhead and compromised utility or accuracy, necessitating ongoing research to refine these methods.”
    Quote in the paperSubstantial claim
  • Premisecitation

    The risk of privacy infringement due to misuse of pseudo-anonymized patient data is a primary concern.

    “The risk of privacy infringement due to the misuse of pseudo-anonymized patient data [7,8] is a primary concern.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [7] Wang, C., Zhang, J., Lassi, N., and Zhang, X. (2022). Privacy Protection in Using Artificial Intelligence for Healthcare: Chinese Regulation in Comparative Perspective. Healthcare, 10. source

      Not checked in the free run.

    • cites [8] Panagopoulos, 2022, Incentivizing the Sharing of Healthcare Data in the AI Era, Comput. Law Secur. Rev. source

      Not checked in the free run.

  • Premisecitation

    The study conducted at a university hospital highlights the potential of AI to improve healthcare delivery and decision-making in high-pressure, ethically sensitive situations.

    “The study conducted at a university hospital highlights the potential of AI to improve healthcare delivery and decision-making, especially in high-pressure, ethically sensitive situations.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [6] Biller-Andorno, N., Ferrario, A., Jöbges, S., Krones, T., Massini, F., Barth, P., Arampatzis, G., and Krauthammer, M. (2020). AI Support for Ethical Decision-Making around Resuscitation: Proceed with Care. Medrxiv (Cold Spring Harb. Lab.), preprint. source

      Not checked in the free run.

  • Classified as probably not a substantial claim. 91
  • May combine two claims. 115

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    65 articles were excluded for various reasons.

    “Out of these, 65 were excluded for various reasons: some were not related to healthcare, some did not discuss AI, some did not address privacy challenges, some did not discuss large-scale data processing, Differential Privacy, or anonymization schemes, some did not discuss patient perceptions, some were published before the specified date range, some were not published in English, and some had low”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    300 articles were found outside the scope of the review.

    “However, 300 of these articles were found to be outside the scope of the review.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    483 duplicate articles were removed.

    “In the second step, 483 duplicate articles were removed, and the abstracts of the remaining 392 articles were manually reviewed for relevance to the research question.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The initial literature search yielded 875 articles.

    “The initial search of information sources yielded 875 articles from various databases, all of which were deemed relevant to the research question by the AI tool.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    392 abstracts were manually reviewed for relevance.

    “In the second step, 483 duplicate articles were removed, and the abstracts of the remaining 392 articles were manually reviewed for relevance to the research question.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    92 articles were thoroughly reviewed.

    “In the third step, the remaining 92 articles were thoroughly reviewed.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    The rapid pace of advancements in AI may have led to new developments postdating the studies reviewed.

    “A primary constraint is the rapid pace of advancements in Artificial Intelligence, which may have led to new developments postdating the studies reviewed.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Findingnone given

    The literature lacks in-depth studies evaluating the real-world effectiveness of various Privacy-Preserving Machine Learning (PPML) techniques within healthcare settings.

    “The literature lacks in-depth studies evaluating the real-world effectiveness of various Privacy-Preserving Machine Learning (PPML) techniques within healthcare settings.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Findingreasoning

    While this review synthesizes critical studies on the responsible integration of AI in healthcare, it also highlights substantial knowledge gaps.

    “While this review synthesizes critical studies on the responsible integration of AI in healthcare, it also highlights substantial knowledge gaps.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Findingnone given

    There is a notable deficiency in studies that delve into the perspectives of patients and healthcare professionals on AI and privacy.

    “There is a notable deficiency in studies that delve into the perspectives of patients and healthcare professionals on AI and privacy, which are crucial for shaping informed and responsive policies.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    The research does not extensively cover potential vulnerabilities and threats to privacy in AI-driven healthcare systems.

    “Additionally, the research does not extensively cover potential vulnerabilities and threats to privacy in AI-driven healthcare systems.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Findingnone given

    Exploring AI's ethical and legal implications in healthcare, particularly concerning privacy, data protection, and algorithmic discrimination, is not exhaustive.

    “Furthermore, exploring AI's ethical and legal implications in healthcare, particularly concerning privacy, data protection, and algorithmic discrimination, is not exhaustive.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    AI systems have shown proficiency in structuring vast medical data, identifying clinical patterns, and validating medical hypotheses.

    “For instance, AI systems have shown remarkable proficiency in structuring vast medical data, identifying clinical patterns, and validating medical hypotheses.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [4] Morley, J., Machado, C.C.V., Burr, C., Cowls, J., Taddeo, M., and Floridi, L. (2019). The Debate on the Ethics of AI in Health Care: A Reconstruction and Critical Review. Soc. Sci. Res. Netw. source

      Not checked in the free run.

    • cites [5] Prakash, S., Balaji, J.N., Joshi, A., and Surapaneni, K.M. (2022). Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare—A Scoping Review of Reviews. J. Pers. Med., 12. source

      Not checked in the free run.

    • cites [6] Biller-Andorno, N., Ferrario, A., Jöbges, S., Krones, T., Massini, F., Barth, P., Arampatzis, G., and Krauthammer, M. (2020). AI Support for Ethical Decision-Making around Resuscitation: Proceed with Care. Medrxiv (Cold Spring Harb. Lab.), preprint. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    AI can offer computational assistance for complex clinical decisions such as CPR and DNAR status that aligns with patient preferences while mitigating human biases under stress and time pressure.

    “In scenarios like cardio-pulmonary resuscitation and determining Do Not Attempt to Resuscitate (DNAR) status, AI can offer computational assistance that aligns with patient preferences while mitigating human biases in decision-making under stress and time pressure [4][5][6].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [4] Morley, J., Machado, C.C.V., Burr, C., Cowls, J., Taddeo, M., and Floridi, L. (2019). The Debate on the Ethics of AI in Health Care: A Reconstruction and Critical Review. Soc. Sci. Res. Netw. source

      Not checked in the free run.

    • cites [5] Prakash, S., Balaji, J.N., Joshi, A., and Surapaneni, K.M. (2022). Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare—A Scoping Review of Reviews. J. Pers. Med., 12. source

      Not checked in the free run.

    • cites [6] Biller-Andorno, N., Ferrario, A., Jöbges, S., Krones, T., Massini, F., Barth, P., Arampatzis, G., and Krauthammer, M. (2020). AI Support for Ethical Decision-Making around Resuscitation: Proceed with Care. Medrxiv (Cold Spring Harb. Lab.), preprint. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The review adhered to PRISMA guidelines.

    “In conducting this literature review, we adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines outlined in [3].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [3] Page, 2021, The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews, Br. Med. J. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    The need for large volumes of data to train AI systems poses inherent risks of privacy infringement.

    “The need for large volumes of data to effectively train and operate AI systems poses inherent risks of privacy infringement.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI systems in healthcare rely on extensive patient data.

  • May combine two claims.

    Premisecitation

    These AI systems can analyze vast data sets to generate suggestions consistent with patient preferences and likely outcomes, potentially reducing the impact of stress, time pressure, personal biases, and conflicts of interest.

    “These systems can analyze vast data sets to generate suggestions consistent with patient preferences and likely outcomes, potentially reducing the impact of stress, time pressure, personal biases, and conflicts of interest that may affect human decision-making.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [6] Biller-Andorno, N., Ferrario, A., Jöbges, S., Krones, T., Massini, F., Barth, P., Arampatzis, G., and Krauthammer, M. (2020). AI Support for Ethical Decision-Making around Resuscitation: Proceed with Care. Medrxiv (Cold Spring Harb. Lab.), preprint. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Differential Privacy introduces controlled noise to data sets to obscure individual identities, permitting the training of AI models on population-representative data without compromising individual privacy.

    “Lastly, Differential Privacy (DP) introduces controlled noise to data sets to obscure individual identities, permitting the training of AI models on population-representative data without compromising individual privacy [10].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [10] Zarifis, 2020, Evaluating If Trust and Personal Information Privacy Concerns Are Barriers to Using Health Insurance That Explicitly Utilizes AI, J. Internet Commer. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Federated Learning allows for training AI models on decentralized devices or servers that hold local data samples, negating the need to transfer sensitive patient data and thus reducing centralization concerns.

    “In parallel, Federated Learning (FL) allows for training AI models on decentralized devices or servers that hold local data samples, negating the need to transfer sensitive patient data and thus reducing centralization concerns [13].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [13] Rahman, 2022, Federated Learning-Based AI Approaches in Smart Healthcare: Concepts, Taxonomies, Challenges and Open Issues, Clust. Comput. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisereasoning

    Smart contracts can automate the consent process for data sharing, ensuring that patient data are only accessed by authorized parties and under specific conditions agreed upon by the patient.

    “Smart contracts, which are self-executing contracts with the terms of the agreement directly written into code, can automate the consent process for data sharing, ensuring that patient data are only accessed by authorized parties and under specific conditions agreed upon by the patient.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisereasoning

    Blockchain technology has emerged as a promising solution to enhance data security and integrity in healthcare AI.

    “Blockchain technology has emerged as a promising solution to enhance data security and integrity in healthcare AI.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Blockchain technology provides a secure, decentralized method for storing and managing healthcare data, using a distributed ledger system to ensure patient records are immutable and verifiable, thereby thwarting unauthorized access and potential data breaches.

    “Blockchain technology provides a secure, decentralized method for storing and managing healthcare data, utilizing a distributed ledger system to ensure that patient records are immutable and verifiable, thereby thwarting unauthorized access and potential data breaches.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [7] Wang, C., Zhang, J., Lassi, N., and Zhang, X. (2022). Privacy Protection in Using Artificial Intelligence for Healthcare: Chinese Regulation in Comparative Perspective. Healthcare, 10. source

      Not checked in the free run.

    • cites [12] Pereira, T., Morgado, J., Silva, F., Pelter, M.M., Dias, V., De Cássia Nogueira Barros, R., De Freitas, C., Negrão, E., De Lima, B.F., and Da Silva, M.C. (2021). Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare, 9. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Blockchain can be integrated with AI and IoT devices to enable secure and real-time monitoring of patient health data, providing healthcare professionals with reliable data for decision-making while preserving patient privacy.

    “Moreover, blockchain can be integrated with other technologies, such as AI and IoT devices, to enable secure and real-time monitoring of patient health data, providing healthcare professionals with reliable data for decision-making while preserving patient privacy [7,12].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [7] Wang, C., Zhang, J., Lassi, N., and Zhang, X. (2022). Privacy Protection in Using Artificial Intelligence for Healthcare: Chinese Regulation in Comparative Perspective. Healthcare, 10. source

      Not checked in the free run.

    • cites [12] Pereira, T., Morgado, J., Silva, F., Pelter, M.M., Dias, V., De Cássia Nogueira Barros, R., De Freitas, C., Negrão, E., De Lima, B.F., and Da Silva, M.C. (2021). Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare, 9. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Premisereasoning

    In healthcare, blockchain can create a secure and unforgeable record of patient data transactions, ensuring that medical records are accurate, consistent, and tamper-proof.

    “In healthcare, blockchain can create a secure and unforgeable record of patient data transactions, ensuring that medical records are accurate, consistent, and tamper-proof.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The complexity of AI algorithms often makes it challenging to track patient data utilization within these systems, leading to potential misuse or a lack of transparency.

    “The complexity of AI algorithms often makes it challenging to track patient data utilization within these systems [9,10], leading to potential misuse or a lack of transparency.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

    • cites [10] Zarifis, 2020, Evaluating If Trust and Personal Information Privacy Concerns Are Barriers to Using Health Insurance That Explicitly Utilizes AI, J. Internet Commer. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Premisenone given

    Blockchain's decentralized nature eliminates the need for central authority, reducing the risk of a single point of failure that could compromise the entire system.

    “Its decentralized nature eliminates the need for central authority, reducing the risk of a single point of failure that could compromise the entire system.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Homomorphic Encryption offers a solution for performing computations on encrypted data without decryption, allowing AI to process and learn from the data while it remains encrypted, thus upholding privacy even when third-party analysis is required.

    “Homomorphic Encryption (HE) offers a solution for performing computations on encrypted data without decryption, allowing AI to process and learn from the data.At the same time, it remains encrypted, thus upholding privacy even when third-party analysis is required [9].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    Blockchain enables secure data sharing among verified entities.

    “It also enables secure data sharing among verified entities.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Collectively, these technologies form a robust framework for protecting patient privacy in the age of AI-driven healthcare.

    “Collectively, these technologies form a robust framework for protecting patient privacy in the age of AI-driven healthcare.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    [15] proposed an architecture combining AI and blockchain to protect smart healthcare systems from data integrity attacks, focusing on wearable devices.

    “The authors of [15] proposed an architecture combining AI and blockchain to protect smart healthcare systems from data integrity attacks, focusing on wearable devices.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [15] Alabdulatif, A., Khalil, I., and Rahman, M.S. (2022). Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis. Appl. Sci., 12. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Differential Privacy provides a mathematical framework guaranteeing that the outcome of an analysis is not significantly affected by the inclusion or exclusion of any individual's data.

    “DP provides a mathematical framework that guarantees that the outcome of an analysis is not significantly affected by the inclusion or exclusion of any individual's data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [13] Rahman, 2022, Federated Learning-Based AI Approaches in Smart Healthcare: Concepts, Taxonomies, Challenges and Open Issues, Clust. Comput. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    [17] provided a bibliometric analysis highlighting the synergy between AI and blockchain in enhancing security and calling for more research to develop stable systems.

    “The authors of [17] provided a bibliometric analysis highlighting the synergy between AI and blockchain in enhancing security and calling for more research to develop stable systems.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] Shinde, R., Patil, S., Kotecha, K., and Ruikar, K. (2021). Blockchain for Securing AI Applications and Open Innovations. J. Open Innov. Technol. Mark. Complex., 7. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    [16] envisioned a healthcare metaverse where AI and blockchain work together to offer secure and efficient remote health monitoring and virtual consultations.

    “The authors of [16] envisioned a healthcare metaverse where AI and blockchain work together to offer secure and efficient remote health monitoring and virtual consultations.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [16] Ali, S., Armand, T.P.T., Athar, A., Hussain, A., Ali, M., Muhammad, Y., Joo, M.-I., and Kim, H.C. (2023). Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security. Sensors, 23. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Since each institution trains a local model on its own data, the global model benefits from a wide range of data sources, leading to more accurate and robust AI models.

    “Since each institution trains a local model on its own data, the global model benefits from a wide range of data sources, leading to more accurate and robust AI models.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [13] Rahman, 2022, Federated Learning-Based AI Approaches in Smart Healthcare: Concepts, Taxonomies, Challenges and Open Issues, Clust. Comput. source

      Not checked in the free run.

    • Rests on an assumption: Data quality and consistency across different healthcare institutions can vary greatly, leading to biased or non-generalizable AI models.

  • May combine two claims.

    Premisecitation

    Challenges in Federated Learning include statistical variance in data distribution among clients, communication efficiency, and potential for malicious clients.

    “The authors of [19] highlight the statistical variance in data distribution among clients, communication efficiency, and the potential for malicious clients as operational challenges in FL.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Xu, 2020, Federated Learning for Healthcare Informatics, J. Healthc. Inform. Res. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisereasoning

    Techniques such as bootstrapping and squashing are employed to manage noise and maintain the integrity of encrypted data in Homomorphic Encryption.

    “Techniques such as bootstrapping and squashing are employed to manage this noise and maintain the integrity of the encrypted data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Differential Privacy introduces a calculated amount of random noise to data or outputs, making it statistically challenging for attackers to deduce sensitive information about any individual in the dataset.

    “It functions by introducing a calculated amount of random noise, either to the data itself or to the outputs of algorithms, thereby making it statistically challenging for attackers to deduce sensitive information about any individual in the dataset.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Xu, 2020, Federated Learning for Healthcare Informatics, J. Healthc. Inform. Res. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The core principle of Differential Privacy is to ensure that the presence or absence of any individual's data does not substantially alter the outcome of an analysis.

    “The core principle of DP is to ensure that the presence or absence of any individual's data do not substantially alter the outcome of an analysis.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Xu, 2020, Federated Learning for Healthcare Informatics, J. Healthc. Inform. Res. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Integration of Homomorphic Encryption into existing healthcare IT systems poses challenges including compatibility with existing software and hardware, staff training, and cost of adoption.

    “Healthcare providers must navigate the complexities of implementing HE within their current workflows, ensuring compatibility with existing software and hardware, and training staff to use the new systems effectively.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Homomorphic Encryption operations are computationally intensive; encryption and decryption processes and computations on encrypted data are resource-intensive and can be significantly slower than operations on plaintext.

    “The encryption and decryption processes and the computations on encrypted data are resource-intensive and can be significantly slower than operations on plaintext.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Homomorphic Encryption allows for secure data sharing between healthcare providers and researchers, fostering collaboration and innovation while protecting patient privacy.

    “It also allows for secure data sharing between healthcare providers and researchers, fostering collaboration and innovation while protecting patient privacy.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Scalability of Homomorphic Encryption solutions is an area of focus; HE methods must scale to handle increasing data volume while maintaining the same level of security and privacy.

    “As healthcare systems generate vast amounts of data, the HE methods must be able to scale accordingly to handle the increasing volume while maintaining the same level of security and privacy.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Homomorphic Encryption facilitates the development of more accurate and personalized predictive models, which can lead to better patient outcomes.

    “By enabling secure and private data analysis, HE facilitates the development of more accurate and personalized predictive models, which can lead to better patient outcomes.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Blockchain creates a secure and tamper-proof ledger for medical data transactions, ensuring the integrity and traceability of patient data.

    “By creating a secure and tamper-proof ledger for medical data transactions, blockchain technology ensures the integrity and traceability of patient data, which is paramount in clinical settings.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Xu, 2020, Federated Learning for Healthcare Informatics, J. Healthc. Inform. Res. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Blockchain can be used to securely manage electronic health records, providing a reliable source of patient data for healthcare professionals and AI algorithms while preventing unauthorized access and data breaches.

    “For instance, blockchain can be used to securely manage electronic health records (EHRs), providing a reliable source of patient data for healthcare professionals and AI algorithms while preventing unauthorized access and data breaches [19].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Xu, 2020, Federated Learning for Healthcare Informatics, J. Healthc. Inform. Res. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    As privacy protection intensifies with increased noise, the usefulness of the data for research and the accuracy of AI models may be compromised.

    “As privacy protection intensifies with increased noise, the usefulness of the data for research and the accuracy of AI models may be compromised.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [19] Xu, 2020, Federated Learning for Healthcare Informatics, J. Healthc. Inform. Res. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Federated Learning enables the training of AI models on decentralized data sources, ensuring that sensitive patient information remains within the confines of the local institution, thereby preserving privacy.

    “By enabling the training of AI models on decentralized data sources, FL ensures that sensitive patient information remains within the confines of the local institution, thereby preserving privacy.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Xu, 2020, Federated Learning for Healthcare Informatics, J. Healthc. Inform. Res. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Client selection protocols, model compression, and update reduction strategies aim to improve communication efficiency in Federated Learning.

    “At the same time, client selection protocols, model compression, and update reduction strategies aim to improve communication efficiency.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Xu, 2020, Federated Learning for Healthcare Informatics, J. Healthc. Inform. Res. source

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  • May combine two claims.

    Interpretationcitation

    By integrating Differential Privacy into healthcare AI systems, researchers and clinicians can gain valuable insights from large datasets without risking the exposure of personal health information, thus maintaining patients' trust and complying with stringent privacy regulations such as GDPR and HIPAA.

    “By integrating Differential Privacy into healthcare AI systems, researchers and clinicians can gain valuable insights from large datasets without risking the exposure of personal health information, thus maintaining patients' trust and complying with stringent privacy regulations such as GDPR and HIPAA [21].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [21] Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R.M., Sheng, Q.Z., Han, M., Zhao, L., Sannino, G., Ranieri, C.M., Ueyama, J., and Batista, D.M. (2023). Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey. Electronics, 12. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Integrating Secure Multi-party Computation and Differential Privacy within Federated Learning frameworks enhances the privacy and security of the data, making Federated Learning a powerful tool for healthcare applications.

    “Moreover, integrating Secure Multi-party Computation and Differential Privacy within FL frameworks enhances the privacy and security of the data, making FL a powerful tool for healthcare applications [19].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Xu, 2020, Federated Learning for Healthcare Informatics, J. Healthc. Inform. Res. source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationcitation

    These technologies enhance the security and privacy of healthcare delivery and ensure that ethical standards are upheld.

    “These technologies enhance the security and privacy of healthcare delivery and ensure that ethical standards are upheld.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [21] Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R.M., Sheng, Q.Z., Han, M., Zhao, L., Sannino, G., Ranieri, C.M., Ueyama, J., and Batista, D.M. (2023). Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey. Electronics, 12. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Integrating blockchain, Federated Learning, Homomorphic Encryption, and Differential Privacy within Internet of Healthcare Things systems offers solutions to challenges of data access restrictions, biases, and high resource demands, ensuring data are managed ethically and securely while enabling innovative healthcare solutions.

    “Integrating blockchain, FL, Homomorphic Encryption, and Differential Privacy within IoHT systems offers solutions to these challenges, ensuring that data are managed ethically and securely while enabling innovative healthcare solutions [15].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [15] Alabdulatif, A., Khalil, I., and Rahman, M.S. (2022). Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis. Appl. Sci., 12. source

      Not checked in the free run.

    • Rests on an assumption: Traditional centralized machine learning models face challenges related to data access restrictions, potential biases, and high resource demands.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationcitation

    Explainable AI plays a foundational role in making AI decisions and processes more comprehensible, thus enhancing transparency and fostering trust.

    “Explainable AI (XAI) plays a foundational role in making AI decisions and processes more comprehensible, thus enhancing transparency and fostering trust.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [13] Rahman, 2022, Federated Learning-Based AI Approaches in Smart Healthcare: Concepts, Taxonomies, Challenges and Open Issues, Clust. Comput. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    AI perceptions in healthcare are tied to how these systems uphold human dignity, highlighting the need for AI applications to respect patient values.

    “The authors of [25] explore how AI perceptions in healthcare are tied to how these systems uphold human dignity, highlighting the need for AI applications to respect patient values.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [25] Formosa, 2022, Medical AI and Human Dignity: Contrasting Perceptions of Human and Artificially Intelligent (AI) Decision Making in Diagnostic and Medical Resource Allocation Contexts, Comput. Hum. Behav. source

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  • May combine two claims.

    Interpretationcitation

    These technologies provide mechanisms to ensure that patient consent is obtained and that data are used to respect individual autonomy and dignity.

    “The ethical considerations of patient data use are also addressed, as these technologies provide mechanisms to ensure that patient consent is obtained and that data are used to respect individual autonomy and dignity [19].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Xu, 2020, Federated Learning for Healthcare Informatics, J. Healthc. Inform. Res. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationcitation

    As models become more accurate, they can provide better predictions and recommendations, improving patient outcomes.

    “As models become more accurate, they can provide better predictions and recommendations, improving patient outcomes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [5] Prakash, S., Balaji, J.N., Joshi, A., and Surapaneni, K.M. (2022). Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare—A Scoping Review of Reviews. J. Pers. Med., 12. source

      Not checked in the free run.

    • Rests on an assumption: The efficacy of AI models heavily depends on the input data's accuracy and reliability.

  • May combine two claims.

    Interpretationcitation

    Homomorphic Encryption and Differential Privacy contribute to a patient-centric approach by allowing for the secure and private analysis of health data, ensuring that patients' identities are shielded from potential misuse.

    “Homomorphic Encryption and Differential Privacy further contribute to this patient-centric approach by allowing for the secure and private analysis of health data, ensuring that patients' identities are shielded from potential misuse [15].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [15] Alabdulatif, A., Khalil, I., and Rahman, M.S. (2022). Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis. Appl. Sci., 12. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The development of AI models that are both fair and explainable allows healthcare providers and patients to understand and trust AI decisions, reinforcing patient autonomy and agency.

    “The development of AI models that are both fair and explainable allows healthcare providers and patients to understand and trust AI decisions, reinforcing patient autonomy and agency.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [24] Angerschmid, 2022, Fairness and Explanation in AI-Informed Decision Making, Mach. Learn. Knowl. Extr. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Restrictions on data flow could hinder the development of future AI applications and diminish the need for data to train them.

    “Such restrictions could hinder the development of future AI applications and diminish the need for data to train them.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [8] Panagopoulos, 2022, Incentivizing the Sharing of Healthcare Data in the AI Era, Comput. Law Secur. Rev. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Existing experiences in monetizing digitalized content, such as music, offer a practical solution to incentivize patients to share their data without restricting data flows.

    “They suggest that existing experiences in monetizing digitalized content, such as music, offer a practical solution to incentivize patients to share their data without restricting data flows.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [8] Panagopoulos, 2022, Incentivizing the Sharing of Healthcare Data in the AI Era, Comput. Law Secur. Rev. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Legal and ethical challenges concerning data privacy and patient consent are highlighted by authors of [28], who stress the importance of robust legal frameworks adaptable to evolving AI.

    “The legal and ethical challenges, particularly concerning data privacy and patient consent, are highlighted by the authors of [28], who stress the importance of robust legal frameworks that are adaptable and responsive to the evolving nature of AI applications.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [28] Vaassen, 2022, AI, Opacity, and Personal Autonomy, Philos. Technol. source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Patient engagement is emphasized, advocating for informed and active involvement in AI-related healthcare decisions.

    “The authors of [27] emphasize patient engagement, advocating for informed and active involvement in AI-related healthcare decisions.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [27] Topol, 2023, The Imperative for Regulatory Oversight of Large Language Models (or Generative AI) in Healthcare, Npj Digit. Med. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Blockchain offers opportunities for enhancing data security and patient privacy but also challenges aligning with GDPR requirements.

    “Blockchain offers opportunities for enhancing data security and patient privacy but also challenges aligning with GDPR requirements.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [29] Kelly, C., Karthikesalingam, A., Suleyman, M., Corrado, G.S., and King, D. (2019). Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Med., 17. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    There is a risk of AI simplifying patient narratives, emphasizing the need for AI systems to augment personalized care while maintaining essential human elements for adequate healthcare.

    “In the broader context of AI decision-making, [26] points out the risk of AI simplifying patient narratives, emphasizing the need for AI systems to augment personalized care while maintaining essential human elements for adequate healthcare.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [26] Kudina, 2021, Regulating AI in Health Care: The Challenges of Informed User Engagement, Hastings Cent. Rep. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Initiatives like Health Data Hub, SOLID, and DECODE aim to control data flow and induce artificial scarcity.

    “The article outlines various initiatives, like the Health Data Hub, SOLID, and DECODE, which aim to control data flow and induce artificial scarcity.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [8] Panagopoulos, 2022, Incentivizing the Sharing of Healthcare Data in the AI Era, Comput. Law Secur. Rev. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Private entities' control of health data in the AI commercial sector, as exemplified by the DeepMind-Royal Free London NHS Foundation Trust case, underscores the need for dynamic and adaptable legal and regulatory frameworks.

    “Furthermore, private entities' control of health data in the AI commercial sector, as exemplified by the DeepMind-Royal Free London NHS Foun-dation Trust case, underscores the need for dynamic and adaptable legal and regulatory frameworks.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [1] Murdoch, B. (2021). Privacy and Artificial Intelligence: Challenges for Protecting Health Information in a New Era. BMC Med. Ethics, 22. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    AIIoT adoption in healthcare is rapid and significantly enhances the quality and effectiveness of healthcare services.

    “The research by [21] highlights the rapid adoption of AIIoT in healthcare, significantly enhancing the quality and effectiveness of healthcare services.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [21] Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R.M., Sheng, Q.Z., Han, M., Zhao, L., Sannino, G., Ranieri, C.M., Ueyama, J., and Batista, D.M. (2023). Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey. Electronics, 12. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    AI is 'data-hungry', so the artificial scarcity approach may not apply to AI.

    “However, this may not apply to AI, as AI is 'data-hungry'.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [8] Panagopoulos, 2022, Incentivizing the Sharing of Healthcare Data in the AI Era, Comput. Law Secur. Rev. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Rigorous verification and validation mechanisms are particularly significant in precision-critical fields like ophthalmology.

    “They advocate for rigorous verification and validation mechanisms, which gain particular significance in precision-critical fields like ophthalmology, as noted in [30].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [30] Tom, 2020, Protecting Data Privacy in the Age of AI-Enabled Ophthalmology, Transl. Vis. Sci. Technol. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Less data lead to fewer AI applications, thus limiting the demand for data to train and furnish these applications.

    “Less data lead to fewer AI applications, thus limiting the demand for data to train and furnish these applications [8].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [8] Panagopoulos, 2022, Incentivizing the Sharing of Healthcare Data in the AI Era, Comput. Law Secur. Rev. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    CDM, as proposed by [8], offers a pathway to balance the need for data to fuel AI development with the rights of individuals to control and benefit from their data.

    “The concept of CDM, as proposed by [8], offers a pathway to balance the need for data to fuel AI development with the rights of individuals to control and benefit from their data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [8] Panagopoulos, 2022, Incentivizing the Sharing of Healthcare Data in the AI Era, Comput. Law Secur. Rev. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    DP injects a controlled amount of noise into data to protect individual privacy yet permits valuable aggregate analysis.

    “The article further scrutinizes DP, a technique that injects a controlled amount of noise into data to protect individual privacy yet permits valuable aggregate analysis-balancing data utility against the imperative of protecting health information.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    PPML emerges as a key solution, enhancing data and machine learning security.

    “Privacy-Preserving Machine Learning (PPML) emerges as a key solution, enhancing data and machine learning security-a vital step in overcoming these barriers.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Together HE, DP, and blockchain enhance data confidentiality, preserve data integrity, and ensure anonymity in AI-driven healthcare systems.

    “Together, these technologies enhance data confidentiality, preserve data integrity, and ensure anonymity in AI-driven healthcare systems.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    HE enables computations on encrypted data without compromising security, useful in sensitive applications such as genomic data analysis.

    “The article navigates through the intricacies of HE, a cryptographic method that enables the performance of calculations on encrypted data without compromising its security, highlighting its utility in sensitive applications such as genomic data analysis, where patient privacy is paramount.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Blockchain technology provides secure, immutable, and transparent transaction recording capabilities.

    “Moreover, the article underscores the transformative impact of blockchain technology in healthcare, detailing its secure, immutable, and transparent transaction recording capabilities.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    HE circumvents this risk by allowing computations on encrypted data, maintaining confidentiality of sensitive patient information such as genomic data throughout processing.

    “HE circumvents this risk by allowing computations on encrypted data, thereby maintaining the confidentiality of sensitive patient information, such as genomic data, throughout the processing phase.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [20] Munjal, 2022, A Systematic Review of Homomorphic Encryption and Its Contributions in Healthcare Industry, Complex Intell. Syst. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Maintaining the confidentiality and integrity of patient data is fundamental for developing reliable healthcare AI systems.

    “It stresses the importance of maintaining the confidentiality and integrity of patient data, which is fundamental for developing reliable healthcare AI systems.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Addition of noise to protect privacy can diminish the quality of data, potentially leading to less accurate models.

    “The addition of noise to protect privacy can, paradoxically, diminish the quality of the data, potentially leading to less accurate models.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    HE faces computational efficiency and implementation complexity challenges.

    “Despite its promising potential, HE faces computational efficiency and implementation complexity challenges.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [20] Munjal, 2022, A Systematic Review of Homomorphic Encryption and Its Contributions in Healthcare Industry, Complex Intell. Syst. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    FL is not immune to privacy breaches, and DP addresses this by injecting controlled noise into data or model updates.

    “However, FL is not immune to privacy breaches, and DP addresses this vulnerability by injecting controlled noise into the data or model updates.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    HE is pivotal in collaborative healthcare research and data sharing, allowing secure sharing of sensitive data among multiple researchers or institutions without compromising privacy.

    “Moreover, HE is pivotal in collaborative healthcare research and data sharing.It allows for the secure sharing of sensitive data among multiple researchers or institutions without compromising privacy.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [20] Munjal, 2022, A Systematic Review of Homomorphic Encryption and Its Contributions in Healthcare Industry, Complex Intell. Syst. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Advanced HE schemes are often based on the lattice-based approach including LWE.

    “The technological underpinnings of advanced HE schemes are often based on the lattice-based approach, including Learning with Errors (LWE).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [20] Munjal, 2022, A Systematic Review of Homomorphic Encryption and Its Contributions in Healthcare Industry, Complex Intell. Syst. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    LWE-based schemes have potential resistance to quantum computing attacks, making them viable for post-quantum cryptography.

    “The robustness of LWE-based schemes is further underscored by their potential resistance to quantum computing attacks, making them a viable option for post-quantum cryptography and ensuring the long-term security of healthcare data [20].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [20] Munjal, 2022, A Systematic Review of Homomorphic Encryption and Its Contributions in Healthcare Industry, Complex Intell. Syst. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    FHE supports an unlimited number of operations on encrypted data.

    “The advent of Fully Homomorphic Encryption (FHE) marked a significant breakthrough by supporting an unlimited number of operations on encrypted data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [20] Munjal, 2022, A Systematic Review of Homomorphic Encryption and Its Contributions in Healthcare Industry, Complex Intell. Syst. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Researchers are exploring a variety of PETs including anonymization, cryptographic methods, and blockchain to bolster privacy and security in IoHT.

    “Researchers are exploring a variety of PETs, including anonymization techniques, cryptographic methods, and blockchain technology, to bolster the privacy and security of data in the IoHT ecosystem [21].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [21] Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R.M., Sheng, Q.Z., Han, M., Zhao, L., Sannino, G., Ranieri, C.M., Ueyama, J., and Batista, D.M. (2023). Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey. Electronics, 12. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Blockchain has been instrumental in the pharmaceutical supply chain, tracking drugs from the manufacturer to the patient, ensuring drug authenticity and preventing counterfeit medications.

    “Additionally, blockchain has been instrumental in the pharmaceutical supply chain, tracking drugs from the manufacturer to the patient, thus ensuring drug authenticity and preventing the infiltration of counterfeit medications [17].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] Shinde, R., Patil, S., Kotecha, K., and Ruikar, K. (2021). Blockchain for Securing AI Applications and Open Innovations. J. Open Innov. Technol. Mark. Complex., 7. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    AI can detect anomalous behavior indicative of a security breach, while blockchain secures data transactions and ensures the integrity of health records.

    “AI can detect anomalous behavior indicative of a security breach, while blockchain secures data transactions and ensures the integrity of health records.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [15] Alabdulatif, A., Khalil, I., and Rahman, M.S. (2022). Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis. Appl. Sci., 12. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Blockchain can enable seamless data exchange while upholding stringent security and privacy standards, whereas traditional systems often struggle with data silos and interoperability challenges.

    “Traditional systems also often struggle with data silos and interoperability challenges, whereas blockchain technology can enable seamless data exchange while upholding stringent security and privacy standards [15].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [15] Alabdulatif, A., Khalil, I., and Rahman, M.S. (2022). Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis. Appl. Sci., 12. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    IoHT encompasses a vast network of connected healthcare devices that collect and transmit data, raising unique privacy and security concerns.

    “The IoHT encompasses a vast network of connected healthcare devices that collect and transmit data, raising unique privacy and security concerns.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [21] Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R.M., Sheng, Q.Z., Han, M., Zhao, L., Sannino, G., Ranieri, C.M., Ueyama, J., and Batista, D.M. (2023). Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey. Electronics, 12. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Blockchain's decentralized approach distributes data across a network of nodes, reducing the risk of a single point of failure and enhancing resilience to attacks.

    “Blockchain's decentralized approach, however, distributes data across a network of nodes, thereby reducing the risk of a single point of failure and enhancing resilience to attacks.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [15] Alabdulatif, A., Khalil, I., and Rahman, M.S. (2022). Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis. Appl. Sci., 12. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Blockchain's inherent features such as immutability and auditability are critical in preventing unauthorized data alterations and tracing the source of attacks, enhancing the overall security posture of healthcare systems.

    “Blockchain's inherent features, such as immutability and auditability, are critical in preventing unauthorized data alterations and tracing the source of attacks, thus enhancing the overall security posture of healthcare systems.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [15] Alabdulatif, A., Khalil, I., and Rahman, M.S. (2022). Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis. Appl. Sci., 12. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning

    Blockchain technology emerges as a complementary solution, offering a tamper-evident ledger for data transactions, thus enhancing data integrity and traceability.

    “Blockchain technology emerges as a complementary solution, offering a tamper-evident ledger for data transactions, thus enhancing data integrity and traceability.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Blockchain technology, or distributed ledger technology, has introduced a paradigm shift in AI-driven healthcare systems, revolutionizing how patient data are stored, accessed, and secured.

    “Blockchain technology, or distributed ledger technology (DLT), has introduced a paradigm shift in AI-driven healthcare systems, revolutionizing how patient data are stored, accessed, and secured.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Tagde, 2021, Blockchain and Artificial Intelligence Technology in E-Health, Environ. Sci. Pollut. Res. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    These blockchain capabilities are instrumental in reducing errors, preventing fraud, and enhancing the trustworthiness of digital healthcare systems.

    “These capabilities are instrumental in reducing errors, preventing fraud, and enhancing the trustworthiness of digital healthcare systems [33].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [33] Haleem, 2021, Blockchain Technology Applications in Healthcare: An Overview, Int. J. Intell. Netw.

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Secure storage of patient records, tracking of medication supply chains, and management of consent for data sharing are bolstered by blockchain's tamper-proof nature.

    “The secure storage of patient records, the tracking of medication supply chains, and the management of consent for data sharing are all bolstered by blockchain's tamper-proof nature, which has significant implications for patient care and privacy.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [33] Haleem, 2021, Blockchain Technology Applications in Healthcare: An Overview, Int. J. Intell. Netw.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Integrating Homomorphic Encryption, Differential Privacy, blockchain technology, and Secure Multi-party Computation converges to form a comprehensive security framework critical for the integrity and reliability of modern healthcare systems.

    “These technologies, each with unique capabilities, converge to form a comprehensive security framework critical for the integrity and reliability of modern healthcare systems.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Blockchain technology provides an immutable and transparent ledger system, ensuring data accuracy and history in the healthcare sector.

    “Blockchain technology revolutionizes data handling with its immutable and transparent ledger system, ensuring data accuracy and history in the healthcare sector.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Secure Multi-party Computation allows for secure and private data computation across multiple parties without exposing individual data inputs.

    “Secure Multi-party Computation (SMPC), a critical subfield of cryptography, allows for secure and private data computation across multiple parties without exposing individual data inputs.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Blockchain's intrinsic features of immutability, transparency, and decentralization have been particularly transformative in healthcare, where the accuracy and confidentiality of patient data are of utmost importance.

    “The intrinsic features of blockchain, such as immutability, transparency, and decentralization, have been particularly transformative in healthcare, where the accuracy and confidentiality of patient data are of utmost importance.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [33] Haleem, 2021, Blockchain Technology Applications in Healthcare: An Overview, Int. J. Intell. Netw.

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Differential Privacy effectively maintains the anonymity of individual data points in large-scale analyses, fostering patient trust and encouraging data sharing for comprehensive medical research.

    “Meanwhile, Differential Privacy effectively maintains the anonymity of individual data points in largescale analyses, fostering patient trust and encouraging data sharing for comprehensive medical research.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    GDPR imposes stringent requirements on personal data processing, focusing on principles such as consent, the right to access, rectification, and the right to be forgotten.

    “GDPR imposes stringent requirements on personal data processing, focusing on principles such as consent, the right to access, rectification, and the right to be forgotten.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationcitation

    The integration of blockchain and AI in healthcare has the potential to significantly improve service efficiency, reduce costs, and democratize access to medical services.

    “The integration of blockchain and AI in healthcare has the potential to significantly improve service efficiency, reduce costs, and democratize access to medical services.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [33] Haleem, 2021, Blockchain Technology Applications in Healthcare: An Overview, Int. J. Intell. Netw.

      Not checked in the free run.

    • cites [34] Lysaght, 2019, AI-Assisted Decision-Making in Healthcare, Asian Bioeth. Rev. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Blockchain's decentralized and immutable ledger ensures that no single entity controls the entire dataset, enhancing security by reducing the risk of data tampering and unauthorized access.

    “The decentralized and immutable ledger that blockchain provides ensures that no single entity controls the entire dataset, enhancing security by reducing the risk of data tampering and unauthorized access.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [33] Haleem, 2021, Blockchain Technology Applications in Healthcare: An Overview, Int. J. Intell. Netw.

      Not checked in the free run.

    • cites [34] Lysaght, 2019, AI-Assisted Decision-Making in Healthcare, Asian Bioeth. Rev. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    In a permissioned blockchain, participants are known and can establish contractual relationships, which facilitates compliance with GDPR's requirements for data processing and protection.

    “In a permissioned blockchain, participants are known and can establish contractual relationships, which facilitates compliance with GDPR's requirements for data processing and protection.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    By enabling better control and management of health records, blockchain technology translates to more efficient patient care, as providers have secure and immediate access to up-to-date patient records.

    “By enabling better control and management of health records, blockchain technology translates to more efficient patient care, as providers have secure and immediate access to up-to-date patient records.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [33] Haleem, 2021, Blockchain Technology Applications in Healthcare: An Overview, Int. J. Intell. Netw.

      Not checked in the free run.

    • cites [34] Lysaght, 2019, AI-Assisted Decision-Making in Healthcare, Asian Bioeth. Rev. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Blockchain's impact on electronic health records is significant, facilitating more accessible communication and control and aiding in better and faster decision-making in healthcare.

    “Blockchain's impact on electronic health records (EHRs) is significant, facilitating more accessible communication and control and aiding in better and faster decision-making in healthcare.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [18] Tagde, 2021, Blockchain and Artificial Intelligence Technology in E-Health, Environ. Sci. Pollut. Res. source

      Not checked in the free run.

    • cites [34] Lysaght, 2019, AI-Assisted Decision-Making in Healthcare, Asian Bioeth. Rev. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The requirement for a common consensus to modify records on a blockchain enhances the security of the data, ensuring that any data modification is transparent and agreed upon by all network participants.

    “The requirement for a common consensus to modify records on a blockchain enhances the security of the data, ensuring that any data modification is transparent and agreed upon by all network participants.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Lysaght, 2019, AI-Assisted Decision-Making in Healthcare, Asian Bioeth. Rev. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Blockchain-based telemonitoring healthcare systems can detect and manage cancerous cells, utilizing smart contracts to ensure data integrity.

    “For instance, blockchainbased telemonitoring healthcare systems can detect and manage cancerous cells, utilizing smart contracts to ensure data integrity.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [18] Tagde, 2021, Blockchain and Artificial Intelligence Technology in E-Health, Environ. Sci. Pollut. Res. source

      Not checked in the free run.

    • cites [34] Lysaght, 2019, AI-Assisted Decision-Making in Healthcare, Asian Bioeth. Rev. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Blockchains are append-only ledgers, meaning that while data can be easily added, its removal is possible only under exceptional circumstances.

    “By their very nature, blockchains are append-only ledgers, meaning that while data can be easily added, its removal is possible only under exceptional circumstances.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Blockchain is not a singular technology but represents a broad class of distributed databases, varying in technical and governance arrangements.

    “Blockchain is not a singular technology but represents a broad class of distributed databases, varying in technical and governance arrangements.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Specific blockchain-based systems like MedRec and MeD Share have been developed to operate on a decentralized basis for maintaining data and claims, providing patients with complete and permanent access to their clinical documents.

    “Specific blockchain-based systems like 'MedRec' and 'MeD Share' have been developed to operate on a decentralized basis for maintaining data and claims, providing patients with complete and permanent access to their clinical documents, and focusing on data protection and privacy across different healthcare stakeholders.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [18] Tagde, 2021, Blockchain and Artificial Intelligence Technology in E-Health, Environ. Sci. Pollut. Res. source

      Not checked in the free run.

    • cites [34] Lysaght, 2019, AI-Assisted Decision-Making in Healthcare, Asian Bioeth. Rev. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    This efficiency saves time and reduces the chances of medical errors, leading to improved patient outcomes.

    “This efficiency saves time and reduces the chances of medical errors, leading to improved patient outcomes [33,34].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [33] Haleem, 2021, Blockchain Technology Applications in Healthcare: An Overview, Int. J. Intell. Netw.

      Not checked in the free run.

    • cites [34] Lysaght, 2019, AI-Assisted Decision-Making in Healthcare, Asian Bioeth. Rev. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The decentralized structure of blockchains enhances resilience through replication and ensures that multiple custodians are involved in the upkeep of the ledger.

    “This decentralized structure of blockchains, with data stored and processed across various nodes, not only enhances resilience through replication but also ensures that multiple custodians are involved in the upkeep of the ledger.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Fundamentally, a blockchain is a shared and synchronized digital database maintained through a consensus algorithm across multiple nodes.

    “Fundamentally, a blockchain is a shared and synchronized digital database maintained through a consensus algorithm across multiple nodes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The technical complexity of blockchain may present barriers to adoption, requiring significant investment in infrastructure and education for healthcare professionals and patients alike.

    “Furthermore, the technical complexity of blockchain may present barriers to adoption, requiring significant investment in infrastructure and education for healthcare professionals and patients alike.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [18] Tagde, 2021, Blockchain and Artificial Intelligence Technology in E-Health, Environ. Sci. Pollut. Res. source

      Not checked in the free run.

    • cites [34] Lysaght, 2019, AI-Assisted Decision-Making in Healthcare, Asian Bioeth. Rev. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The decentralized nature of blockchain allows for the maintenance of EHRs across various organizations, solving issues like data fragmentation and access limitations patients face when their life circumstances change.

    “The decentralized nature of blockchain allows for the maintenance of EHRs across various organizations, solving issues like data fragmentation and access limitations patients face when their life circumstances change.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [18] Tagde, 2021, Blockchain and Artificial Intelligence Technology in E-Health, Environ. Sci. Pollut. Res. source

      Not checked in the free run.

    • cites [34] Lysaght, 2019, AI-Assisted Decision-Making in Healthcare, Asian Bioeth. Rev. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Blockchain's architecture is intentionally resistant to changes; once data are added, it is meant to be permanent.

    “However, the blockchain's architecture is intentionally resistant to changes; once data are added, it is meant to be permanent.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    This diffusion of responsibility challenges the notion of controllership as defined by GDPR, leading to legal ambiguities and difficulties enforcing data protection rights.

    “This diffusion of responsibility challenges the notion of controllership as defined by the GDPR, leading to legal ambiguities and difficulties enforcing data protection rights [32].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    While public blockchains offer pseudonymity, private blockchains usually involve known identities, at least to the network gatekeepers.

    “While public blockchains offer pseudonymity, private blockchains usually involve known identities, at least to the network gatekeepers.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The blockchain ecosystem is multilayered, relying on the Internet and TCP/IP for its operation and serving as an infrastructure for data management.

    “The blockchain ecosystem is multilayered, relying on the Internet and TCP/IP for its operation and serving as an infrastructure for data management.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Blockchains are categorized into public, permissionless, private, and permissioned networks.

    “Blockchains are categorized into public, permissionless, private, and permissioned networks.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Smart contracts can automate processes in healthcare AI, from patient consent management to supply chain tracking.

    “These smart contracts can automate processes in healthcare AI, from patient consent management to supply chain tracking.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationcitation

    Public permissionless blockchains are characterized by transparency, as their complete ledger can be accessed by anyone, enhancing public auditability but reducing privacy.

    “These networks are characterized by transparency, as their complete ledger can be accessed by anyone, enhancing public auditability but reducing privacy.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    This immutability is a cornerstone of blockchain's security and trustworthiness but directly opposes GDPR's requirements for data modification and erasure.

    “This immutability is a cornerstone of blockchain's security and trustworthiness but directly opposes the GDPR's requirements for data modification and erasure.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Blockchain technology is a programmable platform enabling applications such as smart contracts.

    “Blockchain technology goes beyond mere data storage; it is also a programmable platform, enabling applications such as smart contracts.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    The interpretation of the purpose of data processing in a blockchain context, whether it refers only to the initial transaction or extends to ongoing data processing, is yet to be clearly defined.

    “The interpretation of the 'purpose' of data processing in a blockchain context-whether it refers only to the initial transaction or extends to ongoing data processing-is yet to be clearly defined [32].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    By leveraging blockchain, data subjects could monitor how their data are being used, ensuring adherence to GDPR principles like purpose limitation and quickly identifying any breaches or misuse of their data.

    “By leveraging blockchain, data subjects could monitor how their data are being used, ensuring adherence to GDPR principles like purpose limitation and quickly identifying any breaches or misuse of their data [32].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    By utilizing smart contracts, blockchain can automate data transactions, thereby reducing costs and altering the dynamics of data exchange.

    “By utilizing smart contracts, blockchain can automate these transactions, thereby reducing costs and altering the dynamics of data exchange.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Blockchain can potentially empower individuals with greater control over their data, which aligns with GDPR's objectives.

    “Moreover, blockchain can potentially empower individuals with greater control over their data, which aligns with the GDPR's objectives.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationcitation

    The combination of blockchain and advanced data validation algorithms comprehensively addresses the multifaceted challenges of data management and security in healthcare settings.

    “This combination comprehensively addresses the multifaceted challenges of data management and security in healthcare settings.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [29] Kelly, C., Karthikesalingam, A., Suleyman, M., Corrado, G.S., and King, D. (2019). Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Med., 17. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    Integrating these technologies paves the way for AI to augment healthcare delivery with reliability and trustworthiness, addressing national efficiency, patient safety, and treatment outcomes.

    “They pave the way for AI to augment healthcare delivery with the utmost reliability and trustworthiness, addressing national efficiency, patient safety, and treatment outcomes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [29] Kelly, C., Karthikesalingam, A., Suleyman, M., Corrado, G.S., and King, D. (2019). Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Med., 17. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The decentralized and transparent nature of blockchain ensures that data modifications are recorded and traceable, enhancing trust and reliability in the data used by AI systems.

    “The decentralized and transparent nature of blockchain ensures that data modifications are recorded and traceable, enhancing trust and reliability in the data used by AI systems.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] Kelly, C., Karthikesalingam, A., Suleyman, M., Corrado, G.S., and King, D. (2019). Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Med., 17. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Synthea generates realistic but not real medical data, mimicking accurate patient data in structure and composition without corresponding to individual patient information.

    “Synthea, tailored explicitly for research purposes, generates 'realistic but not real medical data', mimicking accurate patient data in structure and composition without corresponding to individual patient information [23].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [23] Olatunji, I.E., Rauch, J., Katzensteiner, M., and Khosla, M. (2022). A Review of Anonymization for Healthcare Data. Big Data. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    These technologies provide a robust data integrity, security, and privacy framework.

    “These technologies provide a robust data integrity, security, and privacy framework.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Integrating SMPC with blockchain technology harnesses the distributed computation power of SMPC along with the secure, transparent ledger of the blockchain, promoting collaborative data analysis across entities while safeguarding privacy and security of each participant's data.

    “Additionally, the integration of Secure Multi-party Computation (SMPC) with blockchain technology harnesses the distributed computation power of SMPC along with the secure, transparent ledger of the blockchain.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Synthetic data generation is vital for maintaining patient privacy and the integrity of AI models, enabling development and testing of healthcare AI applications without the risk of exposing sensitive patient information.

    “This innovative approach is vital for maintaining patient privacy and the integrity of AI models, enabling the development and testing of healthcare AI applications without the risk of exposing sensitive patient information.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [23] Olatunji, I.E., Rauch, J., Katzensteiner, M., and Khosla, M. (2022). A Review of Anonymization for Healthcare Data. Big Data. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Homomorphic Encryption allows for processing encrypted data without decryption, thus maintaining privacy.

    “HE allows for processing encrypted data without decryption, thus maintaining privacy.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Khalid, N., Qayyum, A., Qayyum, A., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications. Comput. Biol. Med., 158. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Synthea and similar tools allow for developing and refining AI models, conducting analyses, and exploring new healthcare technologies while safeguarding patient privacy.

    “By providing rich datasets, Synthea and similar tools allow for developing and refining AI models, conducting analyses, and exploring new healthcare technologies while safeguarding patient privacy.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [23] Olatunji, I.E., Rauch, J., Katzensteiner, M., and Khosla, M. (2022). A Review of Anonymization for Healthcare Data. Big Data. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Synthetic data generation is crucial to maintaining privacy and security in healthcare AI applications, enabling the creation of extensive datasets for research and development while protecting patient privacy and AI model integrity.

    “In conclusion, synthetic data generation is crucial to maintaining privacy and security in healthcare AI applications, enabling the creation of extensive datasets for research and development while protecting patient privacy and AI model integrity.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [12] Pereira, T., Morgado, J., Silva, F., Pelter, M.M., Dias, V., De Cássia Nogueira Barros, R., De Freitas, C., Negrão, E., De Lima, B.F., and Da Silva, M.C. (2021). Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare, 9. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Synthetic data protects patient privacy while preserving data utility.

    “This method protects patient privacy while preserving data utility.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [12] Pereira, T., Morgado, J., Silva, F., Pelter, M.M., Dias, V., De Cássia Nogueira Barros, R., De Freitas, C., Negrão, E., De Lima, B.F., and Da Silva, M.C. (2021). Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare, 9. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Preserving the interactions and correlations in the original data introduces complexity, necessitating comprehensive capture of high-order relationships within the data.

    “Additionally, preserving the interactions and correlations in the original data introduces another layer of complexity, necessitating the comprehensive capture of high-order relationships within the data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [12] Pereira, T., Morgado, J., Silva, F., Pelter, M.M., Dias, V., De Cássia Nogueira Barros, R., De Freitas, C., Negrão, E., De Lima, B.F., and Da Silva, M.C. (2021). Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare, 9. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    AI algorithms' black box nature obscures their decision-making processes, raising concerns about reliability and potential errors.

    “AI algorithms' 'black box' nature, which obscures their decisionmaking processes, raises concerns about reliability and potential errors.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

    • cites [38] Bernal, J., and Mazo, C. (2022). Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci., 12. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Implementing commercial healthcare AI raises legal and ethical issues, including the necessity of patient consent and data protection.

    “Implementing commercial healthcare AI raises several legal and ethical issues, including the necessity of patient consent and data protection.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [1] Murdoch, B. (2021). Privacy and Artificial Intelligence: Challenges for Protecting Health Information in a New Era. BMC Med. Ethics, 22. source

      Not checked in the free run.

    • cites [37] Han, H., and Liu, X. (2021). The Challenges of Explainable AI in Biomedical Data Science. BMC Bioinform., 22. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The deployment of AI in healthcare is marked by challenges like potential errors, biases, and opaque learning algorithms.

    “The deployment of AI in healthcare, marked by challenges like potential errors, biases, and opaque learning algorithms, underscores the importance of tailored regulatory systems.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [37] Han, H., and Liu, X. (2021). The Challenges of Explainable AI in Biomedical Data Science. BMC Bioinform., 22. source

      Not checked in the free run.

    • cites [38] Bernal, J., and Mazo, C. (2022). Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci., 12. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Private corporations' control of patient health information in AI applications poses significant privacy risks, with instances of inadequate privacy protection.

    “Furthermore, private corporations' control of patient health information in AI applications poses significant privacy risks, with instances of inadequate privacy protection calling for stringent regulation and oversight.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [1] Murdoch, B. (2021). Privacy and Artificial Intelligence: Challenges for Protecting Health Information in a New Era. BMC Med. Ethics, 22. source

      Not checked in the free run.

    • cites [37] Han, H., and Liu, X. (2021). The Challenges of Explainable AI in Biomedical Data Science. BMC Bioinform., 22. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    As AI continues to promise significant improvements in health outcomes, its commercial implementation faces privacy challenges.

    “As AI continues to promise significant improvements in health outcomes, its commercial implementation faces privacy challenges.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [1] Murdoch, B. (2021). Privacy and Artificial Intelligence: Challenges for Protecting Health Information in a New Era. BMC Med. Ethics, 22. source

      Not checked in the free run.

    • cites [37] Han, H., and Liu, X. (2021). The Challenges of Explainable AI in Biomedical Data Science. BMC Bioinform., 22. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Advanced AI models like LLMs, including GPT-4, pose new challenges for regulatory bodies like the FDA, which have previously overseen AI-based medical technologies.

    “The emergence of advanced AI models like LLMs, including GPT-4, poses new challenges for regulatory bodies like the FDA, which have previously overseen AI-based medical technologies.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [27] Topol, 2023, The Imperative for Regulatory Oversight of Large Language Models (or Generative AI) in Healthcare, Npj Digit. Med. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    The real-time adaptation of LLMs and the adaptive and autodidactic functions of deep learning algorithms call for regulatory frameworks capable of addressing these systems' adaptability and self-teaching nature.

    “The real-time adaptation of LLMs and the adaptive and autodidactic functions of deep learning algorithms call for regulatory frameworks capable of addressing these systems' adaptability and self-teaching nature.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [27] Topol, 2023, The Imperative for Regulatory Oversight of Large Language Models (or Generative AI) in Healthcare, Npj Digit. Med. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Private and permissioned blockchains generally align more closely with GDPR, while public and permissionless ones often do not.

    “There is a notable distinction between private and permissioned blockchains, which generally align more closely with GDPR, and public and permissionless ones, which often do not.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [25] Formosa, 2022, Medical AI and Human Dignity: Contrasting Perceptions of Human and Artificially Intelligent (AI) Decision Making in Diagnostic and Medical Resource Allocation Contexts, Comput. Hum. Behav. source

      Not checked in the free run.

    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Ensuring transparency and interpretability in AI decision-making processes is essential, albeit challenging, due to the complex nature of AI algorithms.

    “Ensuring transparency and interpretability in AI decision-making processes is also essential, albeit challenging, due to the complex nature of AI algorithms.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [27] Topol, 2023, The Imperative for Regulatory Oversight of Large Language Models (or Generative AI) in Healthcare, Npj Digit. Med. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationcitation

    Certification mechanisms and codes of conduct are important GDPR tools to facilitate the application of principles in specific contexts, especially where personal data are processed.

    “The study underscores the importance of certification mechanisms and codes of conduct as GDPR tools to facilitate the application of these principles in specific contexts, especially where personal data are processed.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Critical areas of ambiguity include the interpretation of concepts like anonymization and the definition of (joint) data controllers, leading to a lack of consensus among supervisory authorities in the European Union.

    “Critical areas of ambiguity include the interpretation of concepts like anonymization and the definition of (joint) data controllers, leading to a lack of consensus among supervisory authorities in the European Union.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Principle-based regulation comprises overarching principles that must be tailored to specific personal data processing operations.

    “This principlebased regulation comprises overarching principles that must be tailored to specific personal data processing operations.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A data governance panel would be responsible for ongoing monitoring of AI systems post-deployment, continuously assessing their performance and ensuring their fairness and alignment with the evolving needs and demographics of the patient population.

    “Additionally, the panel would be responsible for the ongoing monitoring of AI systems post-deployment, continuously assessing their performance and ensuring their fairness and alignment with the evolving needs and demographics of the patient population.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The proposal for establishing a data governance panel in AI healthcare applications highlights the need to mitigate biases in AI systems.

    “Furthermore, the proposal [2] for establishing a data governance panel in AI healthcare applications highlights the need to mitigate biases in AI systems.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    Regulatory lag behind technological advancements, global legal and regulatory framework needs, data privacy concerns in public-private partnerships, and risks of reidentification in big data usage are critical factors that must be considered in developing future healthcare regulations.

    “The regulatory lag behind technological advancements, the global legal and regulatory framework needs, data privacy concerns in public-private partnerships, and the risks of reidentification in big data usage are all critical factors that must be considered in developing future healthcare regulations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The urgency of developing a comprehensive global legal and regulatory framework tailored explicitly for AI in healthcare is underscored by the author of [1].

    “The urgency of developing a comprehensive global legal and regulatory framework tailored explicitly for AI in healthcare is underscored by the author of [1].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1] Murdoch, B. (2021). Privacy and Artificial Intelligence: Challenges for Protecting Health Information in a New Era. BMC Med. Ethics, 22. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    Collective insights emphasize the imperative balance between rapid technological advancements in AI and the ethical considerations guiding their application in healthcare.

    “The collective insights from [1,[36][37][38] emphasize the imperative balance between rapid technological advance-ments in AI and the ethical considerations guiding their application in healthcare.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1,[36][37][38]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationcitation

    This balance is essential for respecting patient privacy and agency, addressing potential biases, and upholding human dignity and integrity in patient care.

    “This balance is essential for respecting patient privacy and agency, addressing potential biases, and upholding human dignity and integrity in patient care.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1,[36][37][38]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Certification mechanisms and codes of conduct could effectively apply GDPR principles to blockchain-specific scenarios.

    “Certification mechanisms and codes of conduct could effectively apply GDPR principles to blockchain-specific scenarios [32].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535.

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Challenges include privacy concerns related to large tech corporations dominating machine learning and neural network technologies, issues surrounding generative models for patient data, and implications of AI technologies in healthcare domains like oncology, organ allocation, and robotic surgery.

    “These challenges include privacy concerns related to large tech corporations dominating machine learning and neural network technologies, issues surrounding generative models for patient data, and the implications of AI technologies in healthcare domains like oncology, organ allocation, and robotic surgery [1].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1] Murdoch, B. (2021). Privacy and Artificial Intelligence: Challenges for Protecting Health Information in a New Era. BMC Med. Ethics, 22. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    The swift advancement of neural networks and advanced machine learning in healthcare, coupled with the rise of blockchain technology, is ushering in a new era of medical diagnostics and treatments powered by robust data processing and analytical capabilities.

    “The swift advancement of neural networks and advanced machine learning in healthcare, coupled with the rise of blockchain technology, is ushering in a new era of medical diagnostics and treatments powered by their robust data processing and analytical capabilities.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The integration of AI in healthcare has brought a paradigm shift in patient consent dynamics, necessitating reevaluation of traditional informed consent models.

    “The integration of Artificial Intelligence (AI) in healthcare has brought about a paradigm shift in the dynamics of patient consent, necessitating a reevaluation of traditional informed consent models to accommodate the unique challenges posed by AI technologies.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    A data governance panel comprising diverse stakeholders including patient representatives, clinical experts, ethicists, and AI specialists would review and monitor datasets used for training AI systems, ensuring datasets encompass a variety of demographic groups, clinical scenarios, and health conditions to avoid biases.

    “Their collective responsibilities would involve reviewing and monitoring the datasets used for training AI systems, ensuring that these datasets encompass a variety of demographic groups, clinical scenarios, and health conditions to avoid biases that could result in skewed or unfair outcomes in healthcare delivery.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Such generative models would generate data not linked to actual individuals, allowing benefits of machine learning without risks of actual patient data.

    “Such models would generate data that are not linked to actual individuals, allowing for the benefits of machine learning without the risks associated with using actual patient data [1,2,22,38].”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [1] Murdoch, B. (2021). Privacy and Artificial Intelligence: Challenges for Protecting Health Information in a New Era. BMC Med. Ethics, 22. source

      Not checked in the free run.

    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

    • cites [22] Keshta, 2022, AI-Driven IoT for Smart Health Care: Security and Privacy Issues, Inform. Med. Unlocked source

      Not checked in the free run.

    • cites [38] Bernal, J., and Mazo, C. (2022). Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci., 12. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Private corporations may have competing goals that do not prioritize data protection, complicating the consent process.

    “These corporations may have competing goals that do not prioritize data protection, thus complicating the consent process.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationcitation

    The potential for biases in AI models, often resulting from unrepresentative data, highlights the critical need to train AI algorithms on diverse and comprehensive datasets.

    “The potential for biases in AI models, often resulting from unrepresentative data, highlights the critical need to train AI algorithms on diverse and comprehensive datasets.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

    • cites [38] Bernal, J., and Mazo, C. (2022). Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci., 12. source

      Not checked in the free run.

    • Rests on an assumption: Training AI algorithms on diverse and comprehensive datasets will prevent or mitigate biases affecting patient care.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Generative models for creating synthetic patient data have been proposed to mitigate ongoing privacy concerns.

    “Additionally, the development of generative models for creating synthetic patient data has been proposed to mitigate ongoing privacy concerns.”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [1] Murdoch, B. (2021). Privacy and Artificial Intelligence: Challenges for Protecting Health Information in a New Era. BMC Med. Ethics, 22. source

      Not checked in the free run.

    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

    • cites [22] Keshta, 2022, AI-Driven IoT for Smart Health Care: Security and Privacy Issues, Inform. Med. Unlocked source

      Not checked in the free run.

    • cites [38] Bernal, J., and Mazo, C. (2022). Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci., 12. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationcitation

    The trustworthiness of AI applications is a cornerstone for their successful integration into healthcare.

    “Furthermore, the trustworthiness of AI applications is a cornerstone for their successful integration into healthcare.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationcitation

    The dynamic nature of AI algorithms necessitates that informed consent be treated as an ongoing process.

    “The dynamic nature of AI algorithms necessitates that informed consent be treated as an ongoing process, with patients allowed to regularly review and renew their consent in line with the evolution of AI applications in healthcare [38].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [38] Bernal, J., and Mazo, C. (2022). Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci., 12. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    AI's 'black-box' nature obscures its inner workings, poses a significant challenge, affecting trust and undermining informed consent.

    “AI's 'black-box' nature, which obscures its inner workings, poses a significant challenge, affecting the level of trust and undermining the foundation of informed consent [2,38].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

    • cites [38] Bernal, J., and Mazo, C. (2022). Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci., 12. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The proliferation of AI-driven IoT in healthcare results in collection of extensive patient data, increasing risk of privacy breaches and unauthorized data exploitation.

    “This concern is exacerbated by the proliferation of the AI-driven Internet of Things (IoT) in healthcare, which results in the collection of extensive patient data, thereby increasing the risk of privacy breaches and unauthorized data exploitation.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [22] Keshta, 2022, AI-Driven IoT for Smart Health Care: Security and Privacy Issues, Inform. Med. Unlocked source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The continuous and dynamic nature of AI systems necessitates an equally flexible and responsive consent process.

    “The continuous and dynamic nature of AI systems, which learn and adapt over time, necessitates an equally flexible and responsive consent process.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

    • cites [22] Keshta, 2022, AI-Driven IoT for Smart Health Care: Security and Privacy Issues, Inform. Med. Unlocked source

      Not checked in the free run.

    • cites [38] Bernal, J., and Mazo, C. (2022). Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci., 12. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    An over-reliance on AI could reduce direct interactions between healthcare professionals and patients, which may adversely affect quality of care and compromise patient autonomy.

    “An over-reliance on AI could reduce direct interactions between healthcare professionals and patients, which may adversely affect the quality of care and compromise patient autonomy [2].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    AI's potential to revolutionize healthcare is vast, offering groundbreaking improvements in patient care, diagnosis, treatment, and overall healthcare management.

    “AI's potential to revolutionize healthcare is undeniably vast, offering groundbreaking improvements in patient care, diagnosis, treatment, and overall healthcare management.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationnone given

    Current techniques for detecting and mitigating biases require further innovation and tailoring to suit the unique healthcare context.

    “While promising, current techniques require further innovation and tailoring to suit the unique healthcare context.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Concerns regarding equity and bias in AI systems are integral to the informed consent dialogue.

    “Concerns regarding equity and bias in AI systems are integral to the informed consent dialogue.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [2] Reddy, 2019, A Governance Model for the Application of AI in Health Care, J. Am. Med. Inform. Assoc. source

      Not checked in the free run.

    • cites [22] Keshta, 2022, AI-Driven IoT for Smart Health Care: Security and Privacy Issues, Inform. Med. Unlocked source

      Not checked in the free run.

    • cites [38] Bernal, J., and Mazo, C. (2022). Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci., 12. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Blockchain is just one element within a larger, more complex ecosystem of solutions required to address the multifaceted challenges posed by AI.

    “However, it is crucial to recognize that blockchain is just one element within a larger, more complex ecosystem of solutions required to address the multifaceted challenges posed by AI.”
    Quote in the paperNot a substantial claim
179 of 179

  • The paper relies on this without support.

    There is a gap between patients' perceptions and actual privacy and security scenarios in AI-integrated healthcare systems.

    “It also explores how these challenges contribute to a gap between patients' perceptions and privacy and security scenarios in AI-integrated healthcare systems.”

  • The paper relies on this without support.

    AI systems in healthcare rely on extensive patient data.

    “AI's reliance on extensive patient data amplifies concerns about data security, informed consent, data ownership, and the risk of unauthorized exploitation.”

  • The paper relies on this without support.

    The 27 articles included in the review are representative of the literature on AI in healthcare privacy.

    “After this rigorous screening process, only 27 of the original 875 articles were deemed suitable for review using the PRISMA template, as shown in Figure 1.”

  • The paper relies on this without support.

    Traditional machine learning approaches often require centralizing data from various sources, which poses significant privacy risks.

    “Traditional machine learning approaches often require centralizing data from various sources, which poses significant privacy risks.”

  • The paper relies on this without support.

    Data quality and consistency across different healthcare institutions can vary greatly, leading to biased or non-generalizable AI models.

    “Data quality and consistency across different healthcare institutions can vary greatly, leading to biased or non-generalizable AI models.”

  • The paper relies on this without support.

    Healthcare data are often siloed across various institutions.

    “As healthcare data are often siloed across various institutions, FL allows for connecting these fragmented data sources while maintaining privacy.”

  • The paper relies on this without support.

    The efficacy of AI models heavily depends on the input data's accuracy and reliability.

    “Ensuring data quality is critical, as the efficacy of AI models heavily depends on the input data's accuracy and reliability.”

  • The paper relies on this without support.

    Traditional centralized machine learning models face challenges related to data access restrictions, potential biases, and high resource demands.

    “However, the traditional centralized machine learning models face challenges related to data access restrictions, potential biases, and high resource demands.”

  • The paper relies on this without support.

    Achieving personalization requires a deep understanding of individual patient data and the ability to adapt AI models accordingly.

    “However, achieving this level of personalization requires a deep understanding of individual patient data and the ability to adapt AI models accordingly.”

  • The paper relies on this without support.

    Models are only as good as the data they learn from.

    “Ensuring high-quality data are essential for the success of FL, as the models are only as good as the data they learn from.”

  • The paper relies on this without support.

    Incorporating diverse data sets into AI model training minimizes biases and promotes equitable health outcomes.

    “Incorporating diverse data sets into AI model training minimizes biases and promotes equitable health outcomes.”

  • The paper relies on this without support.

    Maintaining data integrity and security directly impacts patient safety and the effectiveness of healthcare delivery.

    “This is because it directly impacts patient safety and the effectiveness of healthcare delivery.”

  • The paper relies on this without support.

    Mobile medical applications and wearable devices enable patients to capture personal health data, empowering them to actively manage their health.

    “Mobile medical applications and wearable devices enable patients to capture personal health data, empowering them to actively manage their health.”

  • The paper relies on this without support.

    Active patient involvement in AI-related healthcare decisions fosters a sense of agency and empowerment for patient satisfaction and trust in the healthcare system.

    “Active patient involvement in AI-related healthcare decisions fosters a sense of agency and empowerment for patient satisfaction and trust in the healthcare system.”

  • The paper relies on this without support.

    The exploration of generative models to create synthetic patient data emerges as a promising solution to privacy concerns, enabling the advancement of machine learning without relying on actual patient data.

    “The exploration of generative models to create synthetic patient data emerges as a promising solution to privacy concerns, enabling the advancement of machine learning without relying on actual patient data.”

  • The paper relies on this without support.

    The evolving nature of large language models like GPT-4 underscores the importance of addressing the scale and complexity inherent in these systems to maintain data integrity.

    “The evolving nature of large language models (LLMs) like GPT-4 underscores the importance of addressing the scale and complexity inherent in these systems to maintain data integrity.”

  • The paper relies on this without support.

    The practical implementation of DP in healthcare has been greatly facilitated by the development of open-source tools and libraries, such as Google's TensorFlow Privacy and IBM's Differential Privacy Library.

    “The practical implementation of DP in healthcare has been greatly facilitated by the development of open-source tools and libraries, such as Google's TensorFlow Privacy and IBM's Differential Privacy Library.”

  • The paper relies on this without support.

    Privacy breaches in healthcare have particularly severe implications.

    “The challenges and pitfalls identified in the implementation of DP and other privacy-preserving techniques are critical areas for future research, particularly in the context of healthcare, where the implications of privacy breaches can be particularly severe.”

  • The paper relies on this without support.

    Healthcare systems are becoming increasingly data-driven, which increases the need for robust privacy-preserving mechanisms.

    “As healthcare systems become increasingly data-driven, the need for robust privacy-preserving mechanisms becomes more pronounced.”

  • The paper relies on this without support.

    As healthcare devices become more interconnected, the risk of privacy breaches increases.

    “As healthcare devices become more interconnected, the risk of privacy breaches increases.”

  • The paper relies on this without support.

    Cyber attackers may target patient data for financial gain or disrupt healthcare services for political motives.

    “Cyber attackers may target patient data for financial gain or disrupt healthcare services for political motives.”

  • The paper relies on this without support.

    Traditional centralized data storage systems are commonly used in healthcare.

    “Blockchain technology significantly departs from traditional centralized data storage systems commonly used in healthcare.”

  • The paper relies on this without support.

    Patient autonomy and trust are valuable goals that AI integration in healthcare should preserve.

    “Such a dynamic consent process is vital for preserving patient autonomy and fostering trust in healthcare systems integrating AI technologies.”

  • The paper relies on this without support.

    Informed consent is an appropriate mechanism for protecting patient rights in AI healthcare.

    “The integration of Artificial Intelligence (AI) in healthcare has brought about a paradigm shift in the dynamics of patient consent, necessitating a reevaluation of traditional informed consent models to accommodate the unique challenges posed by AI technologies.”

  • The paper relies on this without support.

    AI integration in healthcare will continue and expand.

    “As AI continues to evolve, our commitment to these principles will ensure its role as a benevolent force in healthcare.”

  • The paper relies on this without support.

    Transparency and explainability of AI systems are achievable and beneficial for informed consent.

    “Enhancing the interpretability of these systems is essential, enabling patients to make truly informed decisions regarding their care.”

  • The paper relies on this without support.

    Training AI algorithms on diverse and comprehensive datasets will prevent or mitigate biases affecting patient care.

    “Such measures are necessary to prevent biases affecting patient care and decision-making.”

  • The paper relies on this without support.

    Patient education can ensure that consent is genuinely informed.

    “Education about the capabilities and limitations of AI is essential to ensuring that consent is genuinely informed.”
28 of 28

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • This decentralized method of federated learning is especially valuable in healthcare, where data sensitivity is highlighted, and regulations like HIPAA and GDPR restrict data sharing.

    “This decentralized method is especially valuable in healthcare, where data sensitivity is highlighted, and regulations like HIPAA and GDPR restrict data sharing [104].”

    From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [104]

    Section 3.3.2 describes federated learning as especially useful for sensitive healthcare data because institutions can train models without exchanging patient data. The paper discusses HIPAA’s protection of health information in §4.2.1 and GDPR requirements elsewhere, but the passages do not establish that both regulations restrict data sharing as the citing sentence states. Page numbers were not available in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Finck, M. (2023, November 15). PE 634.445. Available online: https://data.europa.eu/doi/10.2861/535. · DOI

Cited by

Sorted by citations

Related

Authors
Williamson, S. M., & Prybutok, V.
Affiliations
University of North Texas (US)
Corresponding author
Prybutok, Victor
ORCID
Prybutok, Victor
Venue
Applied Sciences
Publisher
MDPI AG
Published
2024-01-12
Volume
14
Issue
2
Pages
675
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app14020675
OpenAlex
W4390829176
Semantic Scholar
e98c743fe71aa8480bd71ac472fb95264c40496a
Cited by
567
References
37
Citations by year
2026: 178 · 2025: 309 · 2024: 79 · 2022: 1
FWCI
52.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • University of North Texas Digital Library (University of North Texas) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
artificial intelligence in healthcare, patient privacy, differential privacy, data encryption, informed consent, patient rights, algorithmic bias, patient trust, decentralized data sharing, AI governance, data integrity, decision-making autonomy, patient agency

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

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