Clinical Report: A Systematic Review of Federated Learning, Blockchain, and Explainable AI

Overview

This systematic review evaluates the integration of federated learning, blockchain, explainable AI, and incremental optimization for secure healthcare data management. It identifies ten critical issues that must be addressed to enhance data security, interpretability, and efficiency in healthcare systems.

Background

The digitization of healthcare has led to vast collections of sensitive patient data, necessitating secure and efficient data management systems. Current centralized frameworks pose risks of privacy violations and data fragmentation. This review highlights the potential of a hybrid approach combining federated learning, blockchain, explainable AI, and incremental optimization to address these challenges.

Data Highlights

This review synthesizes findings from 26 peer-reviewed studies, indicating an average quality score of 7.0 out of 10 based on the CASP qualitative checklist.

Key Findings

• An integrated architecture can enhance data security and facilitate data sharing.
• Federated learning reduces privacy issues but does not guarantee data integrity.
• Blockchain provides decentralization and immutability, enhancing trust and transparency.
• Explainable AI is crucial for clinical accountability and compliance.
• Incremental optimization allows continuous model improvement without complete retraining.
• Ten critical issues identified include communication costs, scalability, and limited clinical explainability.

Clinical Implications

Healthcare professionals should consider adopting integrated systems that leverage federated learning and blockchain to enhance data security and model interpretability. Addressing the identified critical issues will be essential for the successful implementation of these technologies in clinical settings.

Conclusion

The integration of federated learning, blockchain, explainable AI, and incremental optimization presents a promising approach to secure healthcare data management. Addressing the highlighted challenges will be crucial for advancing these technologies in practice.

Related Resources & Content

1. npj Digital Medicine, 2025 -- Comparing decentralized machine learning and AI clinical models to local and centralized alternatives
2. Intensive Care Medicine, 2024 -- The Limitations of Federated Learning in Addressing Ingrained Biases in Clinical Medicine
3. Intensive Care Medicine, 2024 -- Advancing Data Equity Through Federated Learning in Clinical Settings
4. FDA -- Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles
5. Intensive Care Medicine — Enhanced Data Sharing and AI Model Advancement through Federated Learning in Intensive Care Settings
6. Federated learning for predicting clinical outcomes in patients with COVID-19
7. Blockchain applications in electronic health records: a systematic review
8. Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles | FDA