Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
Open accessAbstract
In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts. This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies, with a particular focus on the healthcare domain. Delving deeply, it explores a multitude of facets including transparency, adept data management, human oversight, educational imperatives, and international collaboration within the realm of AI advancement. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The second contribution of the article is the in-depth and thorough discussion of the limitations inherent to AI systems. It astutely identifies potential biases and the intricate challenges of navigating multifaceted contexts. Lastly, the article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks. Simultaneously, it aptly illustrates the adaptability of the ethical framework proposed herein, positioned skillfully to surmount emergent challenges.
AI snapshot
Written from the paper’s full text.
TL;DR
This article reviews ethical dimensions of AI in healthcare and beyond, arguing for transparency, equity, accountability, and human-centric design, and proposes a six-pillar ethical framework plus governance/collaboration components. It catalogs AI limitations such as black-box opacity, bias, limited contextual and emotional intelligence, and trust/accountability challenges, while calling for globally standardized AI ethics principles.
Key findings
- Proposes a conscientious AI framework emphasizing transparency, equity, answerability, and human-centric orientation, with six pillars: Sensitivity, Evaluation, User-Centricity, Responsibility, Beneficence, and Security.
- Identifies major AI limitations: black-box/explainability problems, bias and fairness concerns (statistical, social, systematic, generalization, and human bias), limited contextual intelligence, erosion of human connection/empathy, and challenges to trust, reliability, and accountability.
- Cites illustrative evidence: an algorithm using healthcare spending would reduce the number of Black patients identified as needing extra care by over 50%; a 2018 London Metropolitan Police facial recognition system identified 104 previously unknown suspects but only 2 were accurately predicted; GPT-4 as a medicine chatbot offers benefits but risks misinformation, overreliance, ethical/legal concerns, and lack of emotional support.
- Calls for globally standardized AI ethics principles and frameworks, and outlines a governance/collaboration ecosystem including Human Oversight and Intervention (HOI), Multi-Stakeholder Engagement (MSE), Privacy by Design (PbD), Safe AI, Ethical Governance Council (EGC), Awareness Programs, Continuous Improvement and Adaptability (CIA), and Inter-Generational Considerations (IGC).
Methods
- Design: comprehensive research article and conceptual review/framework proposal, not an empirical study.
- Data or sample: no primary dataset or sample is reported; the paper draws on literature, examples, and legal frameworks such as GDPR and HIPAA.
- Main analysis: thematic discussion of ethical dimensions, AI limitations, data privacy/consent, and the proposed six-pillar ethical framework and governance/collaboration ecosystem.
Limitations
- The paper states limitations inherent to AI systems—bias, black-box opacity, contextual intelligence, lack of empathy, and trust/accountability challenges—but does not state limitations of its own framework.
- Evident from the text: the proposed framework is conceptual and not empirically validated; no systematic review protocol, sample, or quantitative evaluation is reported.
- Evident from the text: examples are illustrative and drawn from secondary sources, with no primary data collection or validation of the proposed pillars.
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Claims the paper makes
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- Premisecitation
Limitations, particularly in explainability and interpretability, hinder the widespread adoption and integration of AI into clinical practice.
“However, despite these advancements, certain limitations hinder the widespread adoption and integration of AI into clinical practice, particularly in terms of explainability and interpretability [3], [4].”
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- cites [4] Karim, 2018, Machine learning interpretability: A science rather than a tool, arXiv:1807.06722
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- Interpretationreasoning
Insufficient interpretability hinders the widespread and responsible adoption of ML, especially in high-stakes domains where transparency and accountability are paramount.
“Insufficient interpretability hinders the widespread and responsible adoption of ML, especially in high-stakes domains where transparency and accountability are paramount.”
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Lack of interpretability and explainability in ML and DL models can lead to ethical issues and a loss of trust in healthcare.
“In the healthcare context, the lack of interpretability [10] and explainability [11] in ML and DL models can lead to ethical issues and a loss of trust [12], [13].”
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Integrating transparency and interpretability into AI models can instill trust and confidence among healthcare professionals and patients.
“By integrating transparency and interpretability into AI models, these systems can instill trust and confidence among healthcare professionals and patients.”
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The inner workings of black-box models are often complex and challenging to explain in human terms.
“However, the inner workings of these models are often complex and challenging to explain in human terms [38], [39].”
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- cites [39] source
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- Premisecitation
The black-box nature of AI models restricts the transparency of their outputs, making it difficult to comprehend the reasoning behind their decisions.
“The black-box nature of AI models restricts the transparency of their outputs, making it difficult to comprehend the reasoning behind their decisions [18] as illustrated in Figure 1.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [18] Lakkaraju, 2017, Interpretable & explorable approximations of black box models, arXiv:1707.01154
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- Interpretationreasoning
The absence of explainability in AI systems raises ethical concerns.
“The absence of explainability in AI systems also raises ethical concerns.”
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XAI can facilitate regulatory compliance by enabling audits and assessments of AI models to ensure fairness, accountability, and compliance with ethical standards.
“Moreover, it can also facilitate regulatory compliance by enabling audits and assessments of AI models to ensure fairness, accountability, and compliance with ethical standards [29], [30].”
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Lack of interpretability hampers the ability to identify and mitigate biases in the data or algorithms used by AI models.
“Moreover, the lack of interpretability hampers the ability to identify and mitigate biases in the data or algorithms used by AI models.”
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A commonly used algorithm in the U.S. healthcare system exhibited racial bias.
“For instance, [55] highlighted the significance of this issue through their study, which revealed racial bias in a commonly used algorithm in the U.S. health-care system.”
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- Premisecitation
The inability to explain how decisions are made hinders the acceptance and adoption of AI in critical healthcare scenarios.
“The inability to explain how decisions are made hinders the acceptance and adoption of AI in critical healthcare scenarios [4].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] Karim, 2018, Machine learning interpretability: A science rather than a tool, arXiv:1807.06722
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- Premisecitation
The presence of bias regarding AI presents substantial challenges to achieving fairness in healthcare.
“However, the presence of bias regarding AI presents substantial challenges to achieving fairness in healthcare.”
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Many AI algorithms, such as DNNs, operate as black-box models, making it challenging to understand the reasoning behind their decisions.
“Many AI algorithms, such as DNNs, operate as black-box models, making it challenging to understand the reasoning behind their decisions.”
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The black-box nature of DL models presents challenges in understanding their decision-making process.
“However, their black-box nature presents challenges in understanding their decision-making process.”
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In clinical management, lack of transparency regarding the reasoning behind algorithms is problematic because decisions often involve critical, life-or-death situations.
“While this may be acceptable in certain fields such as business decisions or human behavioral studies, it becomes problematic in clinical management, where decisions often involve critical, life-or-death situations [40].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [40] Ghassemi, 2020, A review of challenges and opportunities in machine learning for health, AMIA Summits Transl. Sci. Proc.
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- Rests on an assumption: In healthcare, decisions can have life-and-death consequences.
- Premisecitation
Lack of interpretability can limit the ability to understand why certain decisions are made, leading to potential biases, discrimination, or errors that go unnoticed.
“This lack of interpretability can limit our ability to understand why certain decisions are made, leading to potential biases, discrimination, or errors that go unnoticed [25]”
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- Interpretationreasoning
Lack of interpretability can be a significant barrier to the adoption of AI in healthcare.
“This lack of interpretability can be a significant barrier to the adoption of AI in healthcare.”
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The algorithm relied on healthcare spending to determine patients requiring extra medical attention.
“The algorithm relied on healthcare spending to determine patients requiring extra medical attention.”
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- Premisecitation
Prediction models for cardiovascular disease that claim to predict heart attacks several years in advance are often trained on datasets predominantly comprising male subjects.
“Prediction models for cardiovascular disease that claim to predict heart attacks several years in advance are often trained on datasets predominantly comprising male subjects [58].”
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- Interpretationreasoning
If the dataset used to train AI algorithms is not diverse and representative, it can introduce statistical bias and compromise the accuracy and generalizability of the algorithms.
“However, if the dataset used to train AI algorithms is not diverse and representative, it can introduce statistical bias and compromise the accuracy and generalizability of the algorithms.”
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In dermatology, CNNs used to classify skin lesions are often trained on datasets predominantly composed of samples from patients with fair skin tones.
“For example, in dermatology, CNNs used to classify skin lesions are often trained on datasets predominantly composed of samples from patients with fair skin tones.”
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Similar racial and gender bias can be observed in an online AI image creator tool Playground AI.
“Similar racial and gender bias can be observed in an online AI image creator tool i.e.Playground AI [56] as exemplified in Figure 4.”
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In cardiology, heart attacks are frequently misdiagnosed in women.
“A notable example can be seen in cardiology [57], where heart attacks are frequently misdiagnosed in women.”
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- Premisecitation
Implementing the algorithm would result in a reduction of over 50% in the number of Black patients identified as needing extra care.
“While spending might serve as a reasonable indicator of illness severity and the need for additional care, the authors showed that implementing the algorithm would result in a reduction of over 50% in the number of Black patients identified as needing extra care.”
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Automated sleep scoring algorithms trained on data from young and healthy individuals struggle to accurately diagnose sleep disorders in older patients.
“For example, automated sleep scoring algorithms trained on data from young and healthy individuals struggle to accurately diagnose sleep disorders in older patients.”
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Limited representation of patients with darker skin tones leads to lower diagnostic accuracy when these networks are tested with images of patients with darker skin tones.
“This limited representation of patients with darker skin tones and its variations leads to lower diagnostic accuracy when these networks are tested with images of patients with darker skin tones.”
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There is a higher mortality rate for melanoma in patients with darker skin tones compared to Caucasian patients.
“This disparity is especially problematic given the higher mortality rate for melanoma in patients with darker skin tones compared to Caucasian patients [62].”
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- Premisereasoning
Despite the independent process of analysis conducted by ML algorithms, the potential for bias remains present.
“Despite the independent process of analysis conducted by ML algorithms, the potential for bias remains present.”
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Bias in clinical datasets is not the sole source of bias in healthcare AI.
“While data bias is a critical concern, it is important to note that bias in clinical datasets is not the sole source of bias in healthcare AI.”
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Researchers and clinicians can inadvertently introduce unconscious judgments and biases into their research, which may manifest in biased AI algorithms.
“Researchers and clinicians can inadvertently introduce unconscious judgments and biases into their research, which may manifest in biased AI algorithms.”
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Algorithms used for medical diagnosis or treatment recommendations can exhibit algorithmic bias, leading to biased outcomes.
“In healthcare, algorithms used for medical diagnosis or treatment recommendations can exhibit algorithmic bias, leading to biased outcomes.”
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In a study, racial bias was identified in the initial data, leading to the erroneous conclusion that Black patients are more medically intricate and costly to the healthcare system compared to White patients.
“For instance, in a study conducted by [55] and colleagues, racial bias was identified in the initial data, leading to the erroneous conclusion that Black patients are more medically intricate and costly to the healthcare system compared to White patients.”
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- Premisecitation
Amazon's AI-driven recruitment tool exhibited a negative bias against women due to predominantly male-oriented data used for training.
“Similarly, Amazon's attempt to create an AI-driven tool for recruitment faced problems as the algorithm exhibited a negative bias against women due to the predominantly maleoriented data used for training purposes [70].”
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- Premisecitation
Results obtained from early studies could influence the datasets used to train AI algorithms.
“This means that the results obtained from these early studies could influence the datasets used to train AI algorithms [64], [65].”
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The oversight occurred because the model failed to consider issues of access.
“This oversight occurred because the model failed to consider issues of access.”
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- Interpretationreasoning
Biases during the pre-clinical stages can impact how women respond to newly developed drugs.
“These biases during the pre-clinical stages can impact how women respond to newly developed drugs.”
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A model trained on data from one hospital may not generalize effectively to data from a different hospital due to variations in patient demographics, equipment, and clinical practices.
“For example, a model trained on data from one hospital may not generalize effectively to data from a different hospital due to variations in patient demographics, equipment, and clinical practices [82].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [82] Fang, Rethinking importance weighting for deep learning under distribution shift
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- Premisecitation
If a healthcare provider relies solely on patient ratings and reviews to select a specialist, they may unknowingly favor providers who have received more positive feedback, potentially overlooking highly competent specialists who have fewer online ratings.
“For instance, if a healthcare provider relies solely on patient ratings and reviews to select a specialist, they may unknowingly favor providers who have received more positive feedback, potentially overlooking highly competent specialists who have fewer online ratings [88], [89].”
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If an online health forum primarily attracts individuals with a particular condition who are actively seeking information and support, the data collected from that platform may not reflect the experiences and perspectives of individuals who do not actively engage in online discussions.
“For example, if an online health forum primarily attracts individuals with a particular condition who are actively seeking information and support, the data collected from that platform may not reflect the experiences and perspectives of individuals who do not actively engage in online discussions [87].”
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- Premisecitation
If a particular medication is primarily tested on a specific population (e.g., predominantly White males), there may be limited evidence on how it affects other populations, leading to biased healthcare decisions for those groups.
“For example, if a particular medication is primarily tested on a specific population (e.g., predominantly White males), there may be limited evidence on how it affects other populations, leading to biased healthcare decisions for those groups [71].”
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AI models trained on specific datasets may struggle to generalize their knowledge to new, unseen data in healthcare settings.
“AI models trained on specific datasets may struggle to generalize their knowledge to new, unseen data in healthcare settings [81].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [81] Yang, Rethinking bias-variance trade-off for generalization of neural networks
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If a study on diabetes management predominantly includes data from urban areas, the findings may not accurately represent the experiences and needs of rural populations.
“For instance, if a study on diabetes management predominantly includes data from urban areas, the findings may not accurately represent the experiences and needs of rural populations [86].”
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In healthcare, historical bias can be seen in the underrepresentation of certain demographic groups in clinical trials or medical research.
“In healthcare, historical bias can be seen in the underrepresentation of certain demographic groups in clinical trials or medical research.”
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If an algorithm is trained on biased or incomplete medical data, it may disproportionately impact certain demographic groups, resulting in disparities in diagnosis or treatment recommendations.
“For example, if an algorithm is trained on biased or incomplete medical data, it may disproportionately impact certain demographic groups, resulting in disparities in diagnosis or treatment recommendations [76].”
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In the domain of healthcare, disparities in accessing advanced AI diagnostics can impact patient outcomes.
“In the domain of healthcare, disparities in accessing advanced AI diagnostics can impact patient outcomes [104]- [106].”
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It is still challenging to encode the intricacies of context in AI algorithms.
“It is still challenging to encode the intricacies of context in AI algorithms [96].”
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Biases ingrained in historical data can lead to prejudice in algorithms, perpetuating existing inequalities in areas like hiring and loan approvals.
“Biases ingrained in historical data can lead to prejudice in algorithms, perpetuating the existing inequalities in areas like hiring and loan approvals [102].”
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The digital divide exacerbates discrepancies in accessing AI services and information, particularly for disadvantaged populations.
“The digital divide further exacerbates the issue, creating discrepancies in accessing AI services and information, particularly for disadvantaged populations [103].”
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AI models may not be able to adapt quickly enough to dynamic environments, leading to sub-optimal performance and potential errors.
“AI models may not be able to adapt quickly enough to these dynamic environments, leading to sub-optimal performance and potential errors [98].”
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Achieving human-level contextual understanding in AI remains elusive.
“While AI has shown remarkable advancements in various domains, achieving human-level contextual understanding remains elusive [95].”
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If a study investigates the effectiveness of a particular treatment based on data collected from a specific time period, the findings may not reflect the current healthcare landscape or advancements in medical practices.
“For instance, if a study investigates the effectiveness of a particular treatment based on data collected from a specific time period, the findings may not reflect the current healthcare landscape or advancements in medical practices [91].”
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If a mobile health app is primarily used by individuals who are already health-conscious and actively managing their wellbeing, the data collected may not accurately represent the broader population's health behaviors and needs.
“For example, if a mobile health app is primarily used by individuals who are already health-conscious and actively managing their wellbeing, the data collected may not accurately represent the broader population's health behaviors and needs [86], [90].”
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In 2018, an AI-powered facial recognition system used by the Metropolitan Police in London identified 104 previously unknown suspects, out of which only 2 were accurately predicted.
“In 2018, an AI-powered facial recognition system was used by the Metropolitan Police in London to identify suspects, 104 previously unknown people who were suspected of committing crimes, out of which only 2 were accurately predicted [99].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [99] Santow, 2020, Can artificial intelligence be trusted with our human rights?, AQ, Austral. Quart.
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If electronic health records (EHR) predominantly use medical terminology and abbreviations, the language used in those records may not accurately capture patients' subjective experiences or provide a comprehensive understanding of their health conditions.
“For example, if electronic health records (EHR) predominantly use medical terminology and abbreviations, the language used in those records may not accurately capture patients' subjective experiences or provide a comprehensive understanding of their health conditions [92].”
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Emotional intelligence plays a crucial role in human interactions, decision-making, and overall health.
“It plays a crucial role in human interactions, decision-making, and overall health [127], [128].”
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- cites [128] Beck, 2017, The rise of AI makes emotional intelligence more important, Harvard Bus. Rev.
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In 2018, Amazon's facial recognition system, Rekognition, came under scrutiny for exhibiting racial and gender biases, with significantly higher error rates for identifying darker-skinned individuals and females.
“In 2018, Amazon's facial recognition system, Rekognition, came under scrutiny for exhibiting racial and gender biases.It significantly had higher error rates for identifying darker-skinned individuals and females.”
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- cites [114] West, 2019, Discriminating systems, AI Now
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AI-powered companion robots can sense emotions in residents through facial expressions and gestures.
“These robots can sense emotions in residents through facial expressions and gestures [130].”
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AI-driven healthcare platforms can utilize facial recognition and voice analysis to detect emotions in patients during interactions.
“AI-driven healthcare platforms can utilize facial recognition and voice analysis to detect emotions in patients during interactions [129].”
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AI can assist radiologists in analyzing medical images by quickly identifying anomalies in x-rays or MRIs, flagging potential issues for closer inspection.
“Consider radiology, where AI assists radiologists in analyzing vast volumes of medical images where algorithms can quickly identify anomalies in x-rays or MRIs, flagging potential issues and requiring closer inspection [116].”
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Reports have highlighted instances where IBM's Watson for Oncology suggested treatments that contradicted medical guidelines.
“While it aims to provide evidence-based recommendations, reports have highlighted instances where Watson for Oncology suggested treatments that contradicted medical guidelines [141].”
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- Interpretationcitation
AI-powered virtual assistants and chatbots lack the emotional depth and understanding of human healthcare providers.
“While these tools can streamline communication and provide quick responses, they lack the emotional depth and understanding of human healthcare providers.”
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Hospitals like Mount Sinai in New York have implemented AI systems that analyze patient data to predict potential deterioration hours before it occurs, allowing medical teams to intervene promptly.
“Hospitals like Mount Sinai in New York have implemented AI systems that analyze patient data to predict potential deterioration hours before it occurs, allowing medical teams to intervene promptly [142].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [142] Shukla, 2022, Pandemic analytics: how countries are leveraging big data analytics and artificial intelligence to fight COVID-19?, Social Netw. Comput. Sci.
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- Interpretationreasoning
Overreliance on AI for emotional support could have negative consequences, especially for individuals with severe mental health conditions.
“Overreliance on AI for emotional support could have negative consequences, especially for individuals with severe mental health conditions.”
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The lack of emotional intelligence in AI systems is another obstacle to achieving effective human-AI interaction.
“Thus, the lack of emotional intelligence in AI systems is another obstacle to achieving effective human-AI interaction.”
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Google's Duplex faced criticism for not always disclosing its AI identity during interactions.
“Google's Duplex, an AI system capable of making phone calls and scheduling appointments on behalf of users, faced criticism for not always disclosing its AI identity during interactions [135], [136], highlighting the importance of transparency and ethical considerations in AI development.”
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AI can mimic certain aspects of emotional intelligence.
“Moreover, if, AI can mimic certain aspects of emotional intelligence [137], [138], the ethical and societal implications are noteworthy.”
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- cites [138] Boden, 2016
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The FDA regulatory approach to AI in healthcare seeks to ensure that AI applications meet the same safety and effectiveness standards as traditional medical devices, holding them accountable.
“The Food and Drug Administration (FDA) regulatory approach to AI in healthcare, for example, seeks to ensure that AI applications meet the same safety and effectiveness standards as traditional medical devices.This approach ensures that AI systems, such as those used for diagnosing diseases or making treatment recommendations, are held accountable for their performance and outcomes [143].”
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HIPAA grants patients the right to access and amend their health information under §164.524.
“Furthermore, HIPAA grants patients the right to access and amend their health informa- tion under §164.524.”
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The principle of data minimization, as outlined in GDPR Article 5-1c, comes into play when AI is applied to extensive data analytics.
“The principle of data minimization, as outlined in GDPR Article 5-1c, comes into play when AI is applied to extensive data analytics.”
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HIPAA requires that consent forms be written in plain language, clearly explaining the purpose of data usage and disclosing the individuals or entities that may access the data.
“It requires that consent forms be written in plain language, clearly explaining the purpose of data usage and disclosing the individuals or entities that may access the data.”
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HIPAA emphasizes patients' consent for the usage of their protected health information (PHI), most notably in Article 164.508.
“One of the key ethical considerations within HIPAA is the emphasis on patients' consent for the usage of their protected health information (PHI).This emphasis is outlined in various sections of HIPAA, most notably in Article 164.508, which pertains to the conditions for disclosure of PHI.”
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Consent cannot serve as a valid legal basis for personal data processing when a significant imbalance exists between the data subject and the controlling entity.
“3) Freedom of Consent Criterion: Consent cannot serve as a valid legal basis for personal data processing when a significant imbalance exists between the data subject and the controlling entity.”
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HIPAA generally takes precedence over state laws regarding health information privacy.
“It generally takes precedence over state laws regarding health information privacy.”
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If a state law offers stricter privacy protections than HIPAA, then both the state law and HIPAA must be followed.
“However, if a state law offers stricter privacy protections, then both the state law and HIPAA must be followed §160.203.”
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GDPR's accountability principle, detailed in Article 5-2, extends its reach to AI applications.
“Crucially, GDPR's accountability principle, detailed in Article 5-2, extends its reach to AI applications.”
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Research use of PHI is allowed under certain conditions, with safeguards such as Institutional Review Board (IRB) approval.
“Research use of PHI is allowed under certain conditions, with safeguards such as Institutional Review Board (IRB) approval.”
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Article 20's right to data portability, permitting the transfer of personal data, can affect algorithmic accuracy and confidentiality, potentially influencing the larger dataset.
“Similarly, Article 20's right to data portability, permitting the transfer of personal data, can affect algorithmic accuracy and confidentiality, potentially influencing the larger dataset.”
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Article 15 of GDPR grants individuals the right to access information about their data processing, including details about automated decision-making logic and its consequences.
“Article 15 grants individuals the right to access information about their data processing, including details about automated decision-making logic and its consequences.”
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The minimum necessary standard does not apply when the information is used for treatment, payment, or healthcare operations, among other scenarios.
“However, there are exceptions, for instance, the rule does not apply when the information is used for treatment, payment, or healthcare operations, among other scenarios.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites §164.502[b-1]
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- Premisecitation
Article 22 of GDPR prohibits automated decisions if they produce significant effects.
“Article 22, crucial in the context of AI, prohibits automated decisions if they produce significant effects.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [161] source
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- Premisecitation
Article 17's right to erasure, allowing data removal, can impact an algorithmic model's credibility.
“Article 17's right to erasure, allowing data removal, can impact an algorithmic model's credibility [160].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [160] source
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- Premisecitation
Patients must be given a Notice of Privacy Practices (NPP) outlining how their information can be used, especially for treatment, payment, and health care operations.
“The law also entails that patients must be given a Notice of Privacy Practices (NPP), which outlines how their information can be used, especially for treatment, payment, and health care operations §164.520[b-1][ii-A].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites §164.520[b-1][ii-A]
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- Premisecitation
Article 22-2 provides exceptions, allowing automated decisions if they are contractually necessary, legally authorized with safeguards, or based on explicit consent.
“Article 22-2 provides exceptions, allowing automated decisions if they are contractually necessary, legally authorized with safeguards, or based on explicit consent.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [161] source
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- Premisecitation
Sexual orientation can be inferred from seemingly unrelated data like activity or likes.
“For instance, sexual orientation can be inferred from seemingly unrelated data like activity or likes.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [161] source
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- Premisecitation
HIPAA §164.524[b] grants individuals the right to request amendments to their health records.
“HIPAA goes further to affirm patients' rights to control their health information through §164.524[b], which grants individuals the right to request amendments to their health records.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites §164.524[b]
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- Premisecitation
Under HIPAA §164.524[a], patients are granted the authority to access their own health records held by covered entities.
“Under HIPAA §164.524[a],patients are granted the authority to access their own health records held by covered entities.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites §164.524[a]
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- Premisecitation
HIPAA §164.524[c] outlines the right to receive an accounting of disclosures, which provides patients with information about how their health records have been shared.
“§164.524[c]outlines the right to receive an accounting of disclosures, which provides patients with information about how their health records have been shared.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites §164.524[c]
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- Premisecitation
Non-sensitive data can act as proxies for sensitive data, leading to possible unlawful discrimination.
“Similarly, non-sensitive data can act as proxies for sensitive data, leading to possible unlawful discrimination [161].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [161] source
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- Classified as probably not a substantial claim. 43
- May combine two claims. 36
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PremisecitationAI has shown immense promise in transforming healthcare through the application of advanced technologies like machine learning and deep learning.
“Artificial intelligence (AI) has shown immense promise in transforming healthcare through the application of advanced technologies like machine learning (ML) and deep learning (DL) [1], [2].”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningThe potential for biases embedded within algorithms to exacerbate health disparities and perpetuate social injustices underscores the necessity of vigilance in algorithm development.
“The potential for biases embedded within algorithms to exacerbate health disparities and perpetuate social injustices underscores the necessity of vigilance in algorithm development.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationXAI aims to enhance the transparency and interpretability of AI models, providing insights into the decision-making process and enabling clinicians to understand the reasoning behind AI-generated recommendations.
“XAI aims to enhance the transparency and interpretability of AI models, providing insights into the decision-making process and enabling clinicians to understand the reasoning behind AI-generated recommendations [23].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] source
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InterpretationreasoningBiased data or biased model predictions can have serious consequences in healthcare, resulting in inaccurate diagnoses, suboptimal treatments, and potential harm to patients.
“Biased data or biased model predictions can have serious consequences in healthcare, resulting in inaccurate diagnoses, suboptimal treatments, and potential harm to the patients.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationThe application of AI in healthcare brings forth several limitations that need to be addressed for its successful integration into clinical practice.
“The application of AI in healthcare brings forth several limitations that need to be addressed for its successful integration into clinical practice [37].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [37] source
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PremisecitationThe rapid evolution of AI technologies has outpaced the formulation of comprehensive ethical guidelines, raising concerns about potential misuse and unintended consequences.
“The rapid evolution of AI technologies has outpaced the formulation of comprehensive ethical guidelines, raising concerns about the potential misuse and unintended consequences of these powerful tools [32], [33].”
Quote in the paperSubstantial claim0 of 2 sources checkedClassified as probably not a substantial claim. May combine two claims.
Premisenone givenIn healthcare, concerns about AI encompass data privacy and security, algorithmic bias, accountability, and the potential dehumanization of patient care.
“In the realm of healthcare, these concerns encompass a wide spectrum of issues, including data privacy and security, algorithmic bias, accountability, and the potential dehumanization of patient care.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationLIME and SHAP are examples of post-hoc explainability methods commonly used in healthcare.
“Techniques such as LIME [25] and SHAP [26] are examples of post-hoc explainability methods commonly used in healthcare.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [25] source
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- cites [26] Lundberg, A unified approach to interpreting model predictions
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Classified as probably not a substantial claim.
PremisecitationThe need for an ethical framework becomes more pronounced as AI extends beyond healthcare into education, transportation, and governance.
“The need for an ethical framework becomes even more pronounced as AI is poised to extend its influence beyond healthcare into various domains, such as education, transportation, and governance [34].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] source
Not checked in the free run.
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PremisecitationWithout explainability, healthcare professionals may find it challenging to trust and validate the outputs of AI systems, leading to resistance to adopting these technologies.
“Without explainability, healthcare professionals may find it challenging to trust and validate the outputs of AI systems, leading to resistance to adopting these technologies [19].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19] source
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Classified as probably not a substantial claim.
InterpretationreasoningUnderstanding how DL models arrive at their predictions is often elusive due to their black-box nature.
“However, understanding how these models arrive at their predictions is often elusive due to their black-box nature.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationBias can be categorized as statistical or social, and it can lead to unequal outcomes and hinder equitable healthcare provision.
“Bias can be categorized as statistical or social, and it can lead to unequal outcomes and hinder equitable healthcare provision [53], [54].”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationBlack box models might struggle to explain why a particular intervention is recommended for a specific individual, making it challenging to adapt and personalize interventions effectively.
“Black box models might struggle to explain why a particular intervention is recommended for a specific individual, making it challenging to adapt and personalize interventions effectively [50].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [50] source
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PremisecitationCognitive biases and the lack of diversity in training data contribute to inter- and intra-scoring disagreement, posing limitations.
“Cognitive biases and the lack of diversity in the training data contribute to inter-and intra-scoring disagreement, posing limitations [63].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [63] source
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PremisereasoningData limitations pose a critical challenge in healthcare AI, as biased datasets can result in biased algorithmic outputs.
“Data limitations pose a critical challenge in healthcare AI, as biased datasets can result in biased algorithmic outputs.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationCardiovascular disease manifests differently in men and women, and an algorithm primarily trained on male data may not accurately diagnose female patients.
“However, cardiovascular disease manifests differently in men and women, and an algorithm primarily trained on male data may not accurately diagnose female patients [59].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [59] Jameson, 2018
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InterpretationreasoningThese biases can further perpetuate health inequities and hinder the potential benefits of AI in healthcare.
“These biases can further perpetuate health inequities and hinder the potential benefits of AI in healthcare.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenIn healthcare, systematic biases in algorithms can have far-reaching effects on patient care, health outcomes, and equitable access to healthcare services.
“In healthcare, systematic biases in algorithms can have far-reaching effects on patient care, health outcomes, and equitable access to healthcare services.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenData can be susceptible to various types of bias, which can significantly impact healthcare outcomes.
“Therefore, data can be susceptible to various types of bias, which can significantly impact healthcare outcomes while fairness in AI focuses on ensuring that the outcomes and decisions produced by AI systems do not discriminate against specific groups or individuals based on their inherent characteristics, such as race, gender, age, or socioeconomic status.”
Quote in the paperNot a substantial claimMay combine two claims.
Premisenone givenClinical trials often have predominantly male participants from limited age groups and similar ethnic backgrounds, leading to gender and ethnic biases.
“Clinical trials often have predominantly male participants from limited age groups and similar ethnic backgrounds, leading to gender and ethnic biases.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationThe dataset in [80] does not contain images from different ethnicities, which hinders model generalization.
“for instance the data collected in [80] does not contain the images from different ethnicities which will hinder models generalization.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [80] source
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InterpretationcitationThe facial recognition system was found to be biased against people of color because it was trained on a dataset not representative of the population of London.
“So, the system was found to be biased against people of color.This happened as the system was trained on a dataset that was not representative of the population of London.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [99] Santow, 2020, Can artificial intelligence be trusted with our human rights?, AQ, Austral. Quart.
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PremisecitationIn 2020, an AI-powered self-driving car was involved in a fatal accident because it was unable to understand the context of the situation and collided with a pedestrian.
“In 2020, an AI-powered selfdriving car was involved in a fatal accident.The car was unable to understand the context of the situation and it collided with a pedestrian [100].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [100] source
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PremisecitationAI models, particularly those based on statistical learning, might struggle with common-sense reasoning, making them less effective in handling unfamiliar or ambiguous situations.
“AI models, particularly those based on statistical learning, might struggle with common-sense reasoning, making them less effective in handling unfamiliar or ambiguous situations [97].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [97] source
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PremisecitationThe increasing integration of AI in healthcare has raised concerns about the potential erosion of human connection and empathy in patient care.
“The increasing integration of AI in healthcare has raised concerns about the potential erosion of human connection and empathy in patient care.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [126] Kamensky, 2019, Artificial intelligence and technology in health care: Overview and possible legal implications, DePaul J. Health Care L
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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationIn 2019, Amazon's Alexa was criticized for not being accessible to people with disabilities; its responses were not always clear or understandable, and its features were not always compatible with screen readers.
“For instance, in 2019, Amazon's Alexa was criticized for not being accessible to people with disabilities.The voice assistant's responses were not always clear or understandable, and the assistant's features were not always compatible with screen readers [107].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [107] source
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PremisecitationThe integration of EI principles will enhance the user experience of companion robots.
“While the robot's responses are programmed, the integration of EI principles will enhance the user experience [131].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [131] source
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InterpretationreasoningAI's role is to facilitate and augment decision-making but it cannot replace the ethical reflection and empathy that human professionals bring.
“AI's role is to facilitate and augment the decision-making process, but it cannot replace the ethical reflection and empathy that human professionals bring to the table.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningRadiologists' years of training and experience are essential in validating AI findings, understanding clinical context, and making informed diagnostic decisions.
“Thus, the radiologists' years of training and experience are essential in validating the findings, understanding clinical context, and making informed diagnostic decisions.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationIn organ transplantation, AI could assist in matching donors with recipients efficiently, but ethical nuances such as patient preferences, medical histories, and urgency require human judgment.
“In organ transplantation, AI could assist in matching donors with recipients efficiently.However, ethical nuances such as patient preferences, medical histories, and urgency require human judgment [124], [125].”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationAI's challenges in human-AI interaction include understanding complex language, grasping contextual nuances, and providing transparent explanations for decisions.
“These challenges include AI's ability to understand complex language, grasp contextual nuances, and provide transparent explanations for its decisions [109], [110].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [109] Xu, 2021, Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI, arXiv:2105.05424
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- cites [110] source
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PremisecitationMany users found Woebot helpful to have a tool they could access anytime.
“While many users found it helpful to have a tool they could access anytime [133].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [133] source
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May combine two claims.
InterpretationreasoningAI lacks true empathy, relying on algorithms and data patterns to assess patient conditions and recommend treatments.
“Hence, AI lacks true empathy, relying on algorithms and data patterns to assess patient conditions and recommend treatments.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAlphaFold successfully solved the long-standing problem of protein folding, which had hindered progress in biology and medicine for decades.
“For instance, AlphaFold, an AI-powered algorithm, successfully solved the long-standing problem of protein folding, which had hindered progress in biology and medicine for decades.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [144] source
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningThis limitation can hinder AI's ability to respond effectively to patients' emotional needs and concerns, potentially leaving them feeling unheard or emotionally unsupported.
“limitation can hinder its ability to respond effectively to patients' emotional needs and concerns, potentially leaving them feeling unheard or emotionally unsupported.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationSome users reported feeling disconnected and frustrated with the lack of genuine human interaction, and felt the chatbot's responses were robotic and impersonal, leading to a sense of isolation.
“It was also reported feeling disconnected and frustrated with the lack of genuine human interaction.Some users expressed that the chatbot's responses, although based on evidence-based techniques, felt robotic and impersonal, leading to a sense of isolation [134].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [134] source
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InterpretationreasoningPatients may feel detachment and isolation when their interactions become solely AI-mediated, potentially reducing personalized and empathetic care.
“Patients may feel a sense of detachment and isolation when their interactions become solely AI-mediated, potentially reducing the personalized and empathetic care they receive.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationGDPR Article 5-1b draws a vital link between the purpose of data processing and its legality.
“GDPR Article 5-1b draws a vital link between the purpose of data processing and its legality.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites GDPR Article 5-1b
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PremisecitationHealthcare datasets primarily consist of patient information, and strict regulation of medical privacy encompasses the security of medical records and the confidentiality of conversations between healthcare professionals.
“Healthcare datasets primarily consist of patient information, and strict regulation of medical privacy, encompassing the security of medical records and the confidentiality of conversations between healthcare professionals [145], [146].”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningWithout XAI, it becomes challenging to ensure ethical decision-making, informed consent, and accountability for AI-driven recommendations.
“Without XAI, it becomes challenging to ensure ethical decision-making, informed consent, and accountability for AI-driven recommendations.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationData accuracy is emphasized in GDPR Article 5-1d.
“Data accuracy, an essential facet emphasized in GDPR Article 5-1d, has become paramount in the AI landscape.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites GDPR Article 5-1d
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PremisecitationConsent for profiling must be distinct and separate from granting access to a service.
“2) Granularity of Consent Criterion: Consent for profiling must be distinct and separate from granting access to a service.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [152] 2016
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PremisecitationGDPR applies not only within the EU and EEA but also to the transfer of personal data outside these regions.
“GDPR applies not only within the EU and European Economic Area (EEA) but also to the transfer of personal data outside these regions.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [148] 2016
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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe potential applications of AI in healthcare are vast, including rapid diagnosis, personalized care, and reduction of unnecessary outpatient visits, resulting in significant cost savings.
“The potential applications of AI in healthcare are vast, including rapid diagnosis, personalized care, and reduction of unnecessary outpatient visits, resulting in significant cost savings [144].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [144] source
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PremisecitationUnder GDPR Article 5-1a, the focus is on transparent and lawful data processing.
“Under GDPR's Article 5 [155], 5-1a, the focus is on transparent and lawful data processing.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites GDPR Article 5-1a
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PremisecitationThe GDPR grants EU residents the right to request search engines to remove personal information associated with their names from search results.
“The GDPR grants EU residents the right to request search engines to remove personal information associated with their names from search results.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [148] 2016
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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationIn 2018, the GDPR replaced the Data Protection Directive in the EU, establishing comprehensive data protection and privacy regulations.
“In 2018, The General Data Protection Regulation (GDPR) [148] replaced the Data Protection Directive (DPD) in the European Union (EU), establishing comprehensive data protection and privacy regulations.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [148] 2016
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PremisecitationGDPR Article 5-1e stresses the necessity of limiting the retention of personal data to what is essential for processing.
“GDPR Article 5-1e stresses the necessity of limiting the retention of personal data to what is essential for processing.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites GDPR Article 5-1e
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PremisecitationSimilar concerns are discussed in [149], encompassing patient privacy and confidentiality, challenges in obtaining informed consent, limited data ownership, potential inaccuracies, biases, commercialization risks, and security vulnerabilities.
“Similar concerns are also discussed in [149], which encompass patient privacy and confidentiality, challenges in obtaining informed consent, limited data ownership, potential inaccuracies, biases, commercialization risks, and security vulnerabilities.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [149] source
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PremisecitationConsent must allow further processing only if it aligns with a legal basis and remains consistent with the original purpose for data collection.
“1) Specific Consent Criterion: Consent must allow further processing only if it aligns with a legal basis and remains consistent with the original purpose for data collection.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [152] 2016
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PremisecitationSecuring valid consent is crucial when engaging in AI-powered data analytics and profiling, as outlined by GDPR Article 6.
“From the viewpoint of healthcare, securing valid consent is crucial when engaging in AI-powered data analytics and profiling, as outlined by GDPR Article 6.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites GDPR Article 6
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PremisecitationHIPAA was passed in 1996 to strengthen healthcare data protection by the US government.
“The Health Insurance Portability and Accountability Act (HIPAA) [147] was passed in 1996 to strengthen healthcare data protection by the US government.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [147] source
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PremisecitationGDPR Article 5-1f introduces the security principle, mandating the safeguarding of data integrity and confidentiality during processing.
“Furthermore, GDPR Article 5-1f introduces the security principle, mandating the safeguarding of data integrity and confidentiality during processing.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites GDPR Article 5-1f
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PremisecitationCovered entities are generally prohibited from using or disclosing PHI without individual authorization, except in specific cases outlined by the rule.
“Covered entities are generally prohibited from using or disclosing PHI without individual authorization, except in specific cases outlined by the rule.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [157] source
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PremisecitationIndividuals have the right to access their PHI, request amendments, and receive an accounting of disclosures.
“Individuals have the right to access their PHI, request amendments, and receive an accounting of disclosures.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [157] source
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PremisecitationThe HIPAA Privacy Rule defines Protected Health Information (PHI) as any data, in any format, that can identify an individual based on their health, care, or payment history.
“This rule defines Protected Health Information (PHI) as any data, in any format, that can identify an individual based on their health, care, or payment history § §164.501& 160.103.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites §164.501& 160.103
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PremisecitationCovered entities must have contracts with their business associates who handle PHI on their behalf.
“Covered entities must have contracts with their business associates, who handle PHI on their behalf.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [157] source
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PremisecitationHIPAA Privacy Rule §164.514 establishes guidelines for the handling of PHI in the US healthcare system.
“HIPAA's Privacy Rule §164.514establishes guidelines for the handling of PHI in the US healthcare system.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [157] source
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PremisecitationThe exceptions to HIPAA's prohibition on using or disclosing PHI include treatment, payment, healthcare operations, public health activities, and law enforcement purposes.
“These exceptions include treatment, payment, healthcare operations, public health activities, and law enforcement purposes.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [157] source
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PremisecitationThe HIPAA Privacy Rule emphasizes the principle of using only the minimum necessary information for a given purpose.
“The rule emphasizes the principle of using only the minimum necessary information for a given purpose, and it addresses incidental uses and disclosures.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [157] source
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PremisecitationArticle 21's right to object enables individuals to halt data processing, ensuring data minimization and protecting against data misuse.
“Article 21's right to object enables individuals to halt data processing, ensuring data minimization and protecting against data misuse.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [161] source
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PremisecitationThe Minimum Necessary Standard dictates that any communication about a patient should only include the least amount of information needed for its purpose.
“This principle dictates that any communication about a patient should only include the least amount of information needed for its purpose §164.502[b-1].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites §164.502[b-1]
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PremisecitationHIPAA guarantees privacy, accuracy, and accessibility of all electronic PHI under a covered entity's care, transmission, or storage, defends against foreseeable misuses or exposures of ePHI, and ensures staff compliance.
“Moreover, it also guarantees that privacy, accuracy, and accessibility of all electronic PHI under a covered entity's care, transmission, or storage defends against foreseeable misuses or exposures of ePHI, and ensures that its staff complies with these standards §164.306[b][159].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites §164.306[b][159]
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PremisecitationIf all identifying details are stripped from the data, it is no longer considered PHI, and the restrictions do not apply.
“If all identifying details are stripped from the data, it is no longer considered PHI, and the restrictions do not apply §164.514[a].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites §164.514[a]
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PremisecitationHealthcare providers can use or disclose PHI for treatment, payment, and healthcare operations, and while they can obtain consent for each disclosure related to treatment, it is not mandatory.
“Healthcare providers can use or disclose PHI for these purposes, and while they can obtain consent for each disclosure related to treatment, it is not mandatory §164.506[b-1][158].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites §164.506[b-1][158]
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InterpretationcitationHIPAA and GDPR do not directly mention AI, but several of their clauses have relevance to AI applications and can be challenged by new ways that AI processes personal data.
“Moreover, HIPAA and GDPR do not directly mention AI, several of its clauses have relevance to AI applications and can be challenged by new ways that AI processes personal data [165], [166].”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationArticle 22-4 prohibits using sensitive data for automated decisions, with an exception.
“Article 22-4 prohibits using sensitive data for automated decisions, with an exception.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [161] source
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PremisecitationAI can infer sensitive data from non-sensitive data, potentially leading to unauthorized processing.
“Yet, AI can infer sensitive data from non-sensitive data, potentially leading to unauthorized processing.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [161] source
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The paper relies on this without support.
The deployment of deep learning AI methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts.
“In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts.”
The paper relies on this without support.
In healthcare, decisions can have life-and-death consequences.
“In healthcare, where decisions can have life-and-death consequences, it is crucial to have a clear understanding of how AI algorithms arrive at their recommendations.”
The paper relies on this without support.
Evidence-based medicine has long been the gold standard for treatment strategies.
“Evidence-based medicine has long been the gold standard for treatment strategies.”
The paper relies on this without support.
The size and complexity of models, particularly deep neural networks, have increased in pursuit of better predictive performance.
“The size and complexity of models, particularly deep neural networks (DNNs), have increased in pursuit of better predictive performance.”
The paper relies on this without support.
Limited generalization of AI models is a critical challenge in deploying AI systems in healthcare.
“One of the critical challenges in deploying AI systems in healthcare is the limited generalization of models and ensuring their safety.”
The paper relies on this without support.
AI models may have limited exposure to rare medical conditions or unique patient cases during training, compromising their ability to accurately diagnose or treat such cases.
“Moreover, AI models may have limited exposure to rare medical conditions or unique patient cases during training.Consequently, their ability to accurately diagnose or treat such cases may be compromised.”
The paper relies on this without support.
The lack of diversity within AI development teams may result in models that fail to adequately address the diverse needs of the users.
“Moreover, the lack of diversity within AI development teams may result in models that fail to adequately address the diverse needs of the users.”
The paper relies on this without support.
Cost and affordability pose barriers, hindering access to AI advancements for those with limited financial resources.
“Additionally, cost and affordability pose barriers, hindering access to AI advancements for those with limited financial resources.”
The paper relies on this without support.
Cultural and language barriers can restrict the inclusivity and effectiveness of AI systems.
“Cultural and language barriers can also restrict the inclusivity and effectiveness of AI systems.”
The paper relies on this without support.
AI's limited learning from user interactions and difficulty in handling uncertainty and ambiguity pose obstacles to human-AI interaction.
“AI's limited learning from user interactions and difficulty in handling uncertainty and ambiguity also pose obstacles.”
The paper relies on this without support.
Users may develop over-reliance on AI, leading to frustration and reduced trust when AI fails to meet their expectations.
“Users may develop over-reliance on AI, leading to frustration and reduced trust when AI fails to meet their expectations.”
The paper relies on this without support.
Implementing transparent and XAI models fosters trust and accountability.
“Implementing transparent and XAI models fosters trust and accountability.”
The paper relies on this without support.
The effectiveness of privacy protection laws in practice varies.
“However, the effectiveness of these laws in practice varies.”
The paper relies on this without support.
Automated decision-making, profiling, and the utilization of ML techniques are reshaping data processing, yet they bring forth concerns of bias and privacy invasion.
“Automated decision-making, profiling, and the utilization of ML techniques are reshaping data processing, yet they bring forth concerns of bias and privacy invasion.”
The paper relies on this without support.
AI algorithms often process vast amounts of health data to generate insights and recommendations.
“AI algorithms often process vast amounts of health data to generate insights and recommendations.”
The paper relies on this without support.
AI has the potential to perpetuate biases and stereotypes.
“With AI's potential to perpetuate biases, this pillar advocates systems to perpetuate biases and stereotypes, this pillar supports continuous monitoring and mitigation of biases through data curation, algorithm design, and stakeholder diversity.”
The paper relies on this without support.
AI models can be vulnerable in unique ways beyond traditional cybersecurity.
“This goes beyond traditional cybersecurity, the AI models can be vulnerable in unique ways.”
The paper relies on this without support.
Privacy concerns emerge due to the sensitive nature of data processed by AI systems.
“Privacy concerns emerge due to the sensitive nature of data processed by AI systems.”
The paper relies on this without support.
Economic incentives that drive AI development can profoundly influence its direction.
“Though it is not necessarily an ethical consideration, the economic incentives that drive AI development can profoundly influence its direction.”
The paper relies on this without support.
AI continues to grapple with comprehending the nuances and intricacies of human context.
“While AI has made remarkable strides, it continues to grapple with comprehending the nuances and intricacies of human context.”
The paper relies on this without support.
Understanding complex language and emotional nuances remains challenging as AI and humans interact.
“Additionally, as AI and humans interact, understanding complex language and emotional nuances remains challenging.”
The paper relies on this without support.
Educated decisions can only be made when individuals understand the implications and consequences of AI applications.
“Educated decisions can only be made when individuals understand the implications and consequences of AI applications.”
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?
Ethical concerns around data ownership, informed consent, and algorithmic transparency are growing as AI becomes more pervasive in healthcare.
“Additionally, ethical concerns around data ownership, informed consent, and algorithmic transparency are growing as AI becomes more pervasive in healthcare [116].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [116]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Algorithmic bias is defined as independent of input data but attributed to biased data
MinorThe definition excludes bias present in the input data, while the example attributes algorithmic bias to training on biased medical data.
“Algorithmic Bias: refers to biases that are introduced by the algorithm itself and are not present in the input data.”
“For example, if an algorithm is trained on biased or incomplete medical data, it may disproportionately impact certain demographic groups, resulting in disparities in diagnosis or treatment recommendations [76].”
Sensitivity pillar both promotes and opposes bias
MinorThe sensitivity pillar is said to advocate perpetuating biases and stereotypes, but the same passage says it supports mitigating biases and preventing the reinforcement of prejudices.
“this pillar advocates systems to perpetuate biases and stereotypes”
“this pillar supports continuous monitoring and mitigation of biases through data curation, algorithm design, and stakeholder diversity.By fostering sensitivity, AI creators prevent inadvertent reinforcement of societal prejudices”
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Ethics and Transparency in Secure Web Model Generation
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Empowering Patients With AI-Ethical Digital Tools for Health Data Management and Decision
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Advancing AI Literacy in Medical Education: A Medical AI Competency Framework Development
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Trends and Analysis of Artificial Intelligence Research in Latin America (2013–2023)
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AI‐Driven Innovations in Healthcare: Balancing Technology With Ethics
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“Advanced Anxiety Risk Stratification Using Regularized Deep Neural Networks”
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Charting the Landscape of Artificial Intelligence Ethics: A Bibliometric Analysis
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Ethical Implications of Artificial Intelligence: Ensuring Patient Data Security
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Comprehensive Framework for the Global Regulation and Approval of AI-Integrated Medical Devices
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Future prospective of AI in drug discovery
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Multilingual Virtual Healthcare Assistant
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Healthcare Security Challenges Leveraging Generative AI to Transform Cybersecurity
Nadeem, G., Khaliq, A., Ahmed, J., & Aziz, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-6250-1.ch010 · Cited by 3 · see citing works
Coordination transparency: governing distributed agency in AI systems
Bohr, J. (2026) · AI & Society · doi:10.1007/s00146-026-02853-w · Cited by 5 · see citing works
Khatib, I. A., Ndiaye, M., & Shamayleh, A. (2026) · Bulletin of the National Research Centre/Bulletin of the National Research Center · doi:10.1186/s42269-026-01419-6 · Cited by 5 · see citing works
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Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence
Tripathi, S., Sharma, Y., & Kumar, D. (2024) · Reviews on Recent Clinical Trials · doi:10.2174/0115748871330861241030143321 · Cited by 1 · see citing works
Advancing Patient-Centric Care: Harnessing CPS for Smart Hospitals and Healthcare Facilities
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Integration of AI With Blockchain for Healthcare Security
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AI Transformation in the Workplace: A Comprehensive Review of Trends and Future Directions
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Ethical Considerations in Automated Healthcare
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Survey of AI-Driven Advancements in Diagnosis and Clinical Decisions for Healthcare
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A comprehensive survey of AI trust frameworks in healthcare: current state and future directions
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Medisecure: a hybrid approach for enhancing multimedia data protection in healthcare
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Responsible Machine Learning Deployment: Imperative Framework for Ethical Action
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Mapping Research on AI Ethics and Human Rights: A Bibliometric Study with a TCCM-Based Agenda
Singh, J., Thapliyal, S., Kumar, S., Das, S. S., Rawat, S. S., Jha, V. K., Chaudhary, R., Bhagat, R. K., Sharma, S. R., Anand, A., et al. (2026) · F1000Research · doi:10.12688/f1000research.173404.1 · Cited by 2 · see citing works
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Ethical and Legal Concerns of Deepfake Technology in Biomedical Imaging: A Comprehensive Survey
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Adapting AI-Driven Psychotherapy
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- Authors
- Nasir, S., Khan, R. A., & Bai, S.
- Affiliations
- Salim Habib University (PK)
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 31014-31035
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4392172846
- Semantic Scholar
- 5e38fb3c018d4476549d993d38926db2e5c6032a
- Cited by
- 152
- References
- 158
- Citations by year
- 2026: 64 · 2025: 60 · 2024: 28
- FWCI
- 62.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- AI ethics, ethical frameworks, artificial intelligence in healthcare, deep learning, transparency, data management, human oversight, artificial intelligence in education, international cooperation, algorithmic bias, equity, accountability, safety-critical systems
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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