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

Background: The exponential growth of medical data and advancements in artificial intelligence (AI) have accelerated the development of data-driven health care. However, the secure and efficient sharing of sensitive medical data across institutions remains a major challenge due to privacy concerns, data silos, and regulatory restrictions. Traditional centralized systems are prone to data breaches and single points of failure, while existing privacy-preserving techniques face high computational and communication costs. Objective: This study aims to provide a comprehensive review of the recent advances in blockchain-based federated learning (BCFL) within the medical field. By exploring the synergistic integration of federated learning and blockchain, this review evaluates how BCFL enhances data security, supports privacy-preserving cross-institutional collaboration, and facilitates practical applications in health care, including medical data sharing, Internet of Medical Things, public health surveillance, and telemedicine. Methods: We conducted a systematic literature review using databases such as PubMed, IEEE Xplore, Web of Science, and Google Scholar. Boolean logic and domain-specific keywords were used to retrieve studies from 2018 to 2025. After automated deduplication and multistage manual screening, over 100 high-quality papers were included. These works cover BCFL's theoretical foundations, system architectures, application domains, limitations, and future directions. Results: BCFL frameworks combine the decentralized trust and auditability of blockchain with the privacy-preserving collaborative learning capabilities of federated learning. This integration mitigates risks such as model tampering, data leakage, and a lack of incentives in federated systems. Applications span across cross-institutional medical data sharing, Internet of Medical Things, epidemic forecasting, and telemedicine. Architectures including fully coupled, flexibly coupled, and loosely coupled models offer varying trade-offs between efficiency, scalability, and security. Conclusions: BCFL represents a transformative paradigm for secure, collaborative, and privacy-preserving medical AI. By combining decentralized trust, incentive-driven participation, and privacy-enhancing machine learning, BCFL paves the way for next-generation smart health care systems. Despite current technical and practical challenges, BCFL demonstrates strong potential to support precision medicine, global health data collaboration, and large-scale AI deployment in health care.