The Convergence of AI and Blockchain in Modern Healthcare Systems: A Secure Framework for Electronic Health Records
Keywords:
Artificial Intelligence, Blockchain, Electronic Health Records, Healthcare Security, Data Privacy, Smart Healthcare, Machine Learning, Healthcare AnalyticAbstract
The healthcare industry is undergoing a digital transformation driven by the increasing adoption of Artificial Intelligence (AI) and blockchain technologies. Electronic Health Records (EHRs) have become essential for managing patient information; however, concerns regarding data security, privacy, interoperability, and unauthorized access remain significant challenges. Artificial Intelligence enables advanced analytics, disease prediction, clinical decision support, and personalized treatment recommendations, while blockchain offers decentralized, tamper-resistant, and transparent data management capabilities. This study explores the convergence of AI and blockchain in modern healthcare systems and proposes a secure framework for Electronic Health Records. The framework integrates blockchain-based distributed ledgers with AI-powered analytical modules to ensure secure storage, controlled data sharing, and intelligent healthcare services. The proposed model enhances patient privacy, improves data integrity, facilitates interoperability among healthcare providers, and supports real-time medical decision-making. Furthermore, the article discusses implementation challenges, security considerations, and future research opportunities. The findings suggest that combining AI and blockchain technologies can significantly improve healthcare efficiency, trustworthiness, and resilience in the era of digital medicine.
References
Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.
Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.
Kuo, T. T., Kim, H. E., & Ohno-Machado, L. (2017). Blockchain distributed ledger technologies for biomedical and healthcare applications. Journal of the American Medical Informatics Association, 24(6), 1211–1220.
Agbo, C. C., Mahmoud, Q. H., & Eklund, J. M. (2019). Blockchain technology in healthcare. Healthcare, 7(2), 56.
Jiang, F., Jiang, Y., Zhi, H., et al. (2017). Artificial intelligence in healthcare. Stroke and Vascular Neurology, 2(4), 230–243.
Azaria, A., Ekblaw, A., Vieira, T., & Lippman, A. (2016). MedRec: Using blockchain for medical data access and permission management. IEEE Open and Big Data Conference.
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for EHR analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604.
Zhang, P., White, J., Schmidt, D. C., et al. (2018). FHIRChain: Applying blockchain to securely and scalably share clinical data. Computational and Structural Biotechnology Journal, 16, 267–278.
Radanović, I., & Likić, R. (2018). Opportunities for blockchain technology in medicine. Applied Health Economics and Health Policy, 16(5), 583–590.
Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
Li, H., Zhu, L., Shen, M., et al. (2020). Blockchain-based data preservation system for healthcare data. IEEE Access, 8, 108333–108345.
Hasselgren, A., Kralevska, K., Gligoroski, D., et al. (2020). Blockchain in healthcare and health sciences—A scoping review. International Journal of Medical Informatics, 134, 104040.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Misbah Akram
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
