Objective:

To provide a thorough synthesis of Federated Learning, Blockchain, Explainable AI, and Incremental Optimization for developing a secure, scalable, and intelligent healthcare system, emphasizing the role of each technology.

Key Findings:

• An integrated architecture combining Federated Learning, blockchain, Explainable AI, and Incremental Optimization can be designed.
• Ten critical issues were identified, including communication costs, scalability, interoperability, limited clinical explainability, and high computational costs.
• A hybrid approach can improve data security, interpretability, facilitate data sharing, and mitigate data-sharing risks.
• The average quality score of the studies reviewed was 7.0 out of 10, indicating acceptable methodological quality.

Interpretation:

The study highlights the need for an integrated evaluation of multiple technologies to enhance healthcare data management.

Limitations:

• The review is based on a limited number of studies (26) published from 2018 to 2026, which may affect the generalizability of the findings.
• The focus on peer-reviewed studies may exclude relevant findings from other sources, potentially limiting the scope of the review.

Conclusion:

The integration of Federated Learning, blockchain, Explainable AI, and Incremental Optimization presents a promising approach for secure healthcare data management.