The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis
Open accessAbstract
A smart device carries a great amount of sensitive patient data as it offers innovative and enhanced functionalities in the smart healthcare system. Moreover, the components of healthcare systems are interconnected via the Internet, bringing significant changes to the delivery of healthcare services to individuals. However, easy access to healthcare services and applications has given rise to severe risks and vulnerabilities that hamper the performance of a smart healthcare system. Moreover, a large number of heterogeneous devices accumulate data that vary in terms of size and formats, making it challenging to manage the data in the healthcare repository and secure it from attackers who seek to profit from the data. Thus, smart healthcare systems are susceptible to numerous security threats and risks, such as hardware and software-based attacks, system-level attacks, and network attacks that have the potential to place patients’ lives at risk. An analysis of the literature revealed a research gap in that most security surveys on the healthcare ecosystem examined only the security challenges and did not explore the possibility of integrating modern technologies to alleviate security issues in the smart healthcare system. Therefore, in this article, we conduct a comprehensive review of the various most recent security challenges and their countermeasures in the smart healthcare environment. In addition, an artificial intelligence (AI) and blockchain-based secure architecture is proposed as a case study to analyse malware and network attacks on wearable devices. The proposed architecture is evaluated using various performance metrics such as blockchain scalability, accuracy, and dynamic malware analysis. Lastly, we highlight different open issues and research challenges facing smart healthcare systems.
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In the citing paper
“The authors of [15] proposed an architecture combining AI and blockchain to protect smart healthcare systems from data integrity attacks, focusing on wearable devices.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“By leveraging blockchain, healthcare providers can offer a more transparent and patient-controlled exchange of medical data, empowering patients to have a say in who accesses their information.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Homomorphic Encryption in healthcare AI systems enables secure data utilization, ensuring patient confidentiality is maintained even during complex data analysis processes [15].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“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].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“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].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Federated Learning enables the development of more personalized treatment plans by analyzing data from a diverse patient population while keeping individual data localized and private.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Together, these technologies form a robust defense against cyber threats, safeguarding the confidentiality, integrity, and availability of critical healthcare data [15].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“These centralized systems, typically managed by a single entity, are more vulnerable to cyberattacks and unauthorized access.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“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.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“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.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“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].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“AI can detect anomalous behavior indicative of a security breach, while blockchain secures data transactions and ensures the integrity of health records.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
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Citations
Cites
1 retracted
Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques
Mehbodniya, A., Alam, I., Pande, S. D., Neware, R., Rane, K. P., Shabaz, M., & Madhavan, M. V. (2021) · Security and Communication Networks · doi:10.1155/2021/9293877 · Cited by 96
RetractedMetaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions
Chengoden, R., Victor, N., Huynh‐The, T., Yenduri, G., Jhaveri, R. H., Alazab, M., Bhattacharya, S., Hegde, P., Maddikunta, P. K. R., & Gadekallu, T. R. (2023) · IEEE Access · doi:10.1109/access.2023.3241628 · Cited by 569
Bhuiyan, M. N., Rahman, M. M., Billah, M. M., & Saha, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3062630 · Cited by 490
An exhaustive survey on security and privacy issues in Healthcare 4.0
Hathaliya, J. J., & Tanwar, S. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.018 · Cited by 462
Shi, S., He, D., Li, L., Kumar, N., Khan, M. K., & Choo, K. R. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101966 · Cited by 431
5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions
Ahad, A., Tahir, M., & Yau, K.-L. A. (2019) · IEEE Access · doi:10.1109/access.2019.2930628 · Cited by 352
Hijacking an insulin pump: Security attacks and defenses for a diabetes therapy system
Li, C., Raghunathan, A., & Jha, N. K. (2011) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/health.2011.6026732 · Cited by 328
Security and Privacy for the Internet of Medical Things Enabled Healthcare Systems: A Survey
Sun, Y., Lo, F. P.-W., & Lo, B. (2019) · IEEE Access · doi:10.1109/access.2019.2960617 · Cited by 292
Mansour, R. F., Amraoui, A. E., Nouaouri, I., Diaz, V. G., Gupta, D., & Kumar, S. (2021) · IEEE Access · doi:10.1109/access.2021.3066365 · Cited by 255 · cited 1×
“Thus, Mansour et al. [53] applied an AI and Internet of Things (IoT)- based disease diagnosis model for smart healthcare systems.”
BackgroundYaqoob, T., Abbas, H., & Atiquzzaman, M. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914094 · Cited by 236
Cited by
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Williamson, S. M., & Prybutok, V. (2024) · Applied Sciences · doi:10.3390/app14020675 · Cited by 567
Blockchain Technology: Security Issues, Healthcare Applications, Challenges and Future Trends
Wenhua, Z., Qamar, F., Abdali, T.-A. N., Hassan, R. B., Jafri, S. T. A., & Nguyễn, Q. N. (2023) · Electronics · doi:10.3390/electronics12030546 · Cited by 326
AI-enhanced blockchain technology: A review of advancements and opportunities
Ressi, D., Romanello, R., Piazza, C., & Rossi, S. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.103858 · Cited by 264
Metaverse Chronicles: A Bibliometric Analysis of Its Evolving Landscape
Wider, W., Jiang, L., Lin, J., Fauzi, M. A., Li, J., & Chan, C. K. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2227825 · Cited by 152
Blockchain for the Healthcare Supply Chain: A Systematic Literature Review
Fiore, M., Capodici, A., Rucci, P., Bianconi, A., Longo, G., Ricci, M., Sanmarchi, F., & Golinelli, D. (2023) · Applied Sciences · doi:10.3390/app13020686 · Cited by 80
Metadata
- Authors
- Alabdulatif, A., Khalil, I., & Saidur Rahman, M.
- Affiliations
- Qassim University (SA)
- RMIT University (AU)
- Computer Science and Software Engineering, School of Science, RMIT University, Melbourne, VIC 3000, Australia
- Corresponding author
- Alabdulatif, Abdulatif
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2022-10-31
- Volume
- 12
- Issue
- 21
- Pages
- 11039
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4307751771
- Cited by
- 117
- References
- 95
- Citations by year
- 2026: 28 · 2025: 32 · 2024: 38 · 2023: 18 · 2022: 1
- FWCI
- 22.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Qassim University (10118-coc-2020-1-3-I)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- blockchain, artificial intelligence, wearable devices, malware detection, network attacks, patient data security, secure architecture, dynamic malware analysis, blockchain scalability
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