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This paper is in the following e-collection/theme issue:

Digital Health Reviews (3859) Big Data (449) Machine Learning (3348) Blockchain, Distributed Ledger Apps for Health and Medicine (152) Security in Digital Health (86) Responsible Health AI (268) AI Governance and Policy (81)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/79052, first published .
Doctors use augmented reality to visualize a 3D blood vessel model during surgery.

Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review

Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review

Authors of this article:

Xudong Wang1 Author Orcid Image ;   Yi Xie2 Author Orcid Image ;   Xiaoliang Chen1, 3 Author Orcid Image ;   Jiaming Yang1 Author Orcid Image ;   Ruiyuan Li1 Author Orcid Image ;   Weihang Gao1 Author Orcid Image ;   Zineng Yan1 Author Orcid Image ;   Hong Zhou1 Author Orcid Image ;   Zhewei Ye1 Author Orcid Image
Article Authors Cited by (13) Tweetations (2) Metrics

Journals

  1. Wang X, Xie Y, Chen X, Yang J, Li R, Gao W, Yan Z, Zhou H, Ye Z. Correction: Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review. Journal of Medical Internet Research 2026;28:e95788 View
  2. Wei S, Wei X, Pang T, Li D. Blockchain-Enabled Federated Learning: A Dynamic-Grouping Privacy-Preserving Framework. Mathematics 2026;14(9):1534 View
  3. Bhardwaj T, Sumangali K. Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review. Frontiers in Digital Health 2026;8 View
  4. Sosa Iglesias V, Modi N, Purackal R, Shoukat K, Koyun A, Nangolo M, Park K. Multimodal and Explainable Artificial Intelligence for Precision Healthcare: Integrating Federated Learning, Governance, and Affective Computing. Westcliff International Journal of Applied Research 2026:15 View
  5. Ethakota V, Deepthi K. BeDaSH+: A Comprehensive Framework for Blockchain-Enabled Data-Driven Telehealth Security With Federated Learning, TinyML, and GDPR Compliance. IEEE Access 2026;14:144231 View
  6. Aziz A, Kaçmaz S, Aladwani A. Artificial Intelligence-Enabled Medical Diagnosis in the Internet of Medical Things: An End-to-End Survey of Connected Sensing, Distributed Intelligence, Clinical Applications, and Validation. Diagnostics 2026;16(19):3171 View
  7. Di Grezia G, Clemente A, Dugheri S, Mattei V, Gili A, Mari E, Urraro F, Mucci N, Baldassarre A. Blockchain and Internet of Medical Things for Resilient Healthcare. Bioengineering 2026;13(10):1173 View
  8. Ojerinde O, Yusuf J, Saba D, Abubakar R. Blockchain-Enabled Federated Learning for Healthcare: An Empirical Evaluation of the Integrity Boundary, Byzantine Robustness, and Ledger Overhead. Journal of Future Artificial Intelligence and Technologies 2026;3(3):460 View
  9. Ansari M, Kumar S, Tariq U, Heyat M. AI‐Powered Personalization Across Digital Ecosystems: A Trust‐Centric Perspective on Intelligent, Ethical, and Adaptive User‐Centric Systems. Applied AI Letters 2026;7(3) View

Books/Policy Documents

  1. Al-Humaimeedy A. The Blockchain Horizon - Trends, Trajectories, and Tomorrow's Digital Economy [Working Title]. View

Conference Proceedings

  1. Ahmed R, Khan M, Delshadi A, Ahmad N, Hussain M. 2026 International Conference on Data Science, Machine Learning, and Intelligence (DataSciMI). Edge-Intelligent Blockchain Framework for Ultra-Secure and Energy-Efficient Real-Time Patient Monitoring in IoMT Using Hierarchical Federated Learning View
  2. Jayashri R, Venkatesan V, Kumarakrishnan S, Subasree S, Shanmugam M, Nirmaladevi P. 2026 International Conference on System, Computation, Automation and Networking (ICSCAN). Adversarial AutoEncoder-Based Trust-Aware Hybrid Learning Model for Federated Learning for Secure IoMT Networks View
  3. Jeyakarthika M, Vijayalakshmi K. 2026 7th International Conference On Computational Vision and Bio Inspired Computing (ICCVBIC). Deep Learning Techniques for Lung Cancer Detection from CT Images: A Comprehensive Review with Federated Learning and Blockchain-Based Security View