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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[15] proposed an architecture combining AI and blockchain to protect smart healthcare systems from data integrity attacks, focusing on wearable devices.
“The authors of [15] proposed an architecture combining AI and blockchain to protect smart healthcare systems from data integrity attacks, focusing on wearable devices.”
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]
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.
“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]
Homomorphic Encryption in healthcare AI systems enables secure data utilization, ensuring patient confidentiality is maintained even during complex data analysis processes.
“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]
Integrating blockchain, Federated Learning, Homomorphic Encryption, and Differential Privacy within Internet of Healthcare Things systems offers solutions to challenges of data access restrictions, biases, and high resource demands, ensuring data are managed ethically and securely while enabling innovative healthcare solutions.
“Integrating blockchain, FL, Homomorphic Encryption, and Differential Privacy within IoHT systems offers solutions to these challenges, ensuring that data are managed ethically and securely while enabling innovative healthcare solutions [15].”
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]
Homomorphic Encryption and Differential Privacy contribute to a patient-centric approach by allowing for the secure and private analysis of health data, ensuring that patients' identities are shielded from potential misuse.
“Homomorphic Encryption and Differential Privacy further contribute to this patient-centric approach by allowing for the secure and private analysis of health data, ensuring that patients' identities are shielded from potential misuse [15].”
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]
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.
“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]
Together AI and blockchain form a robust defense against cyber threats, safeguarding the confidentiality, integrity, and availability of critical healthcare data.
“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]
Traditional centralized systems, typically managed by a single entity, are more vulnerable to cyberattacks and unauthorized access than blockchain.
“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]
Blockchain's inherent features such as immutability and auditability are critical in preventing unauthorized data alterations and tracing the source of attacks, enhancing the overall security posture of healthcare systems.
“Blockchain's inherent features, such as immutability and auditability, are critical in preventing unauthorized data alterations and tracing the source of attacks, thus enhancing the overall security posture of healthcare systems.”
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]
Blockchain's decentralized approach distributes data across a network of nodes, reducing the risk of a single point of failure and enhancing resilience to attacks.
“Blockchain's decentralized approach, however, distributes data across a network of nodes, thereby reducing the risk of a single point of failure and enhancing resilience to attacks.”
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]
Blockchain can enable seamless data exchange while upholding stringent security and privacy standards, whereas traditional systems often struggle with data silos and interoperability challenges.
“Traditional systems also often struggle with data silos and interoperability challenges, whereas blockchain technology can enable seamless data exchange while upholding stringent security and privacy standards [15].”
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]
AI can detect anomalous behavior indicative of a security breach, while blockchain secures data transactions and ensures the integrity of health records.
“AI can detect anomalous behavior indicative of a security breach, while blockchain secures data transactions and ensures the integrity of health records.”
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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Integrating Blockchain into Sustainable Smart City Services: Motivations and Challenges
Chentouf, F. Z., Mohamed, E., & Said, B. O. U. Z. A. K. R. A. O. U. I. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-032-04114-2_1
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- 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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