Blockchain-Powered Verifiable AI Models for Medical Diagnosis
DOI:
https://doi.org/10.63345/sjaibt.v1.i1.101Keywords:
Blockchain, Verifiable AI, Medical Diagnosis, Federated Learning, Healthcare Data Security, Zero-Knowledge Proofs, Explainable AI, Tamper-Proof RecordsAbstract
The rapid expansion of artificial intelligence (AI) in healthcare has revolutionized diagnostic practices, enabling applications such as tumor detection in medical imaging, genomic analysis, and predictive risk modeling for early disease prevention. Despite these advancements, concerns about the opacity, trustworthiness, and auditability of AI systems remain significant barriers to clinical adoption. Medical practitioners, regulators, and patients increasingly demand systems that not only produce accurate results but also provide verifiable guarantees regarding the integrity and accountability of diagnostic processes. Blockchain technology, with its intrinsic features of decentralization, immutability, and consensus-driven validation, offers a promising solution to these concerns.
This manuscript investigates the integration of blockchain-powered verifiable AI models for medical diagnosis. We present a comprehensive framework that leverages federated learning for decentralized training, blockchain for immutable storage and consensus validation, and zero-knowledge proofs for cryptographic verification of model outputs. The proposed system ensures transparent audit trails, enhances data integrity, protects patient privacy, and simplifies compliance with regulatory frameworks such as HIPAA and GDPR. Through simulated case studies in medical imaging and predictive diagnostics, we demonstrate that blockchain integration improves diagnostic verifiability, reduces susceptibility to adversarial manipulation, and fosters patient-centric trust. While slight computational latency is introduced, the trade-off is justified by significantly stronger guarantees of transparency, reproducibility, and ethical accountability. This research underscores the transformative role of blockchain in shaping the future of verifiable AI-driven healthcare, providing pathways toward more reliable, transparent, and equitable medical diagnostic ecosystems.
Downloads
References
• https://id4d.worldbank.org/sites/id4d-ms8.extcc.com/files/inline-images/Box%2023.png
• https://miro.medium.com/1*9ImevBH8VbB6H2VWbXm3tQ.png
• Albahri, A. S., Hamid, R. A., & Zaidan, A. A. (2020). Blockchain technology in healthcare: A comprehensive review and directions for future research. Applied Sciences, 10(12), 4109. https://doi.org/10.3390/app10124109
• Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., & Guizani, M. (2021). Blockchain for decentralizing AI: Applications, challenges and future directions. IEEE Internet of Things Journal, 8(4), 3045–3064. https://doi.org/10.1109/JIOT.2020.3030934
• Asatiani, A., Malo, P., Nagbøl, P. R., Penttinen, E., & Rinta-Kahila, T. (2021). Blockchain-based applications in healthcare: A systematic review. Health Policy and Technology, 10(3), 100517. https://doi.org/10.1016/j.hlpt.2021.100517
• Aziz, K., Saeed, F., & Ali, R. (2022). Blockchain-based verifiable machine learning models for secure medical diagnosis. Future Generation Computer Systems, 128, 382–394. https://doi.org/10.1016/j.future.2021.10.010
• Chen, M., Hao, Y., Cai, Y., Wang, Y., & Song, J. (2020). Privacy-preserving medical diagnosis using blockchain and federated learning. IEEE Transactions on Industrial Informatics, 16(6), 4156–4165. https://doi.org/10.1109/TII.2019.2944421
• Dinh, T. T. A., & Thai, M. T. (2020). AI and blockchain integration for trustworthy healthcare: Opportunities and challenges. ACM Computing Surveys, 53(6), 1–36. https://doi.org/10.1145/3412374
• Dwivedi, A. D., Srivastava, G., Dhar, S., & Singh, R. (2021). A decentralized privacy-preserving healthcare blockchain for secure data sharing. Computers in Biology and Medicine, 134, 104457. https://doi.org/10.1016/j.compbiomed.2021.104457
• Ghosh, S., Halder, S., & Pal, S. (2023). Blockchain-enabled verifiable AI for medical imaging: A framework for reliable diagnostics. Journal of Biomedical Informatics, 140, 104322. https://doi.org/10.1016/j.jbi.2023.104322
• Guo, Y., Ding, X., & Jin, X. (2022). Federated learning meets blockchain: Enhancing privacy and trust for medical AI. Information Sciences, 587, 620–637. https://doi.org/10.1016/j.ins.2021.12.026
• Hassija, V., Chamola, V., & Guizani, M. (2020). A blockchain-based framework for AI-enabled healthcare. IEEE Access, 8, 143034–143049. https://doi.org/10.1109/ACCESS.2020.3013472
• Jameel, F., Khan, S., Butt, R. A., & Tariq, M. (2022). Secure and verifiable AI in medical diagnosis using zero-knowledge proofs. Computer Standards & Interfaces, 83, 103642. https://doi.org/10.1016/j.csi.2022.103642
• Jiang, Y., Zhao, J., & Sun, X. (2021). Blockchain and AI for medical diagnosis: Challenges and future perspectives. Journal of Network and Computer Applications, 190, 103146. https://doi.org/10.1016/j.jnca.2021.103146
• Kaur, P., Sharma, M., & Singh, G. (2021). Blockchain-based healthcare data management and verifiable AI-driven diagnostic systems. Computers & Electrical Engineering, 95, 107385. https://doi.org/10.1016/j.compeleceng.2021.107385
• Kumar, R., Marchang, N., & Tripathi, R. (2021). A blockchain-based secure healthcare framework for sharing AI-driven diagnostic results. Computer Communications, 165, 180–194. https://doi.org/10.1016/j.comcom.2020.11.016
• Li, H., Xu, L., & Jiang, C. (2022). Decentralized AI models in healthcare with blockchain: Toward verifiable decision-making. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 7390–7403. https://doi.org/10.1109/TNNLS.2022.3146002
• Lin, X., Wang, J., & Liu, Y. (2020). Blockchain-powered AI for trustworthy medical diagnosis and treatment. Journal of Medical Systems, 44, 199. https://doi.org/10.1007/s10916-020-01668-9
• Mohanty, S. P., Singhal, R., & Ranganathan, N. (2023). Explainable and verifiable AI using blockchain in healthcare diagnostics. IEEE Transactions on Computational Social Systems, 10(1), 45–57. https://doi.org/10.1109/TCSS.2023.3243187
• Tanwar, S., Bhatia, Q., Patel, P., Kumari, A., & Gupta, R. (2020). Blockchain and AI-based solutions for healthcare security and trust. Transactions on Emerging Telecommunications Technologies, 31(12), e4075. https://doi.org/10.1002/ett.4075
• Wang, Q., Su, Z., & Zhang, P. (2021). Blockchain-enabled verifiable federated learning for medical image analysis. Pattern Recognition, 122, 108247. https://doi.org/10.1016/j.patcog.2021.108247
• Zhang, Y., Xiong, Z., & Niyato, D. (2024). A survey on blockchain-empowered trustworthy AI for healthcare. ACM Transactions on Internet Technology, 24(2), 1–32. https://doi.org/10.1145/3581412
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Scientific Journal of Artificial Intelligence and Blockchain Technologies
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The license allows re-users to share and adapt the work, as long as credit is given to the author and don't use it for commercial purposes.
