Integrated AI-blockchain architecture improves data security and interpretability in healthcare systems

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Frontiers in Medicine Published June 4, 2026 Medically reviewed June 4, 2026 Study authors: Tanisha Bhardwaj, K. Sumangali DOI ↗ By Dr. Ji-eun Park, MD · Brain, Mind & Pain

Key Takeaway

Consider that integrated AI-blockchain architectures may improve data security and interpretability, but evidence on clinical outcomes is lacking.

This systematic review synthesizes findings from 26 peer-reviewed studies published from 2018 to 2026 on the design of an integrated architecture that incorporates federated learning, blockchain, explainable AI, and incremental optimization for healthcare data management. The review compares this integrated approach to current distributed healthcare systems that use centralized data processing frameworks.

The main results indicate that the hybrid approach can improve data security, boost interpretability, facilitate data sharing, and prevent data-sharing risks. The overall quality of the included studies was assessed as acceptable, with an average score of 7.0 out of 10. However, no pooled effect sizes or quantitative outcomes were reported for specific clinical endpoints.

The authors note a key limitation: current survey literature studies each technology separately without considering how the four technologies can be harnessed to create synergies. This gap limits the ability to draw strong conclusions about the combined architecture's real-world effectiveness.

Clinicians should interpret these findings cautiously, as the review focuses on data management technologies rather than direct patient outcomes. The proposed architecture may offer theoretical advantages, but practical implementation and clinical impact remain to be demonstrated.

Study Details

Study typeMeta analysis

EvidenceLevel 1

PublishedJun 2026

View Original Abstract ↓

Due to the rapid digitization of healthcare systems, there has been a huge collection of sensitive personal data of patients. Thus, secure, privacy-preserving, and efficient data management systems are required. Current distributed healthcare systems increasingly use centralized data processing frameworks that are prone to privacy violations, data fragmentation, and malicious attacks. Despite advances in federated learning, blockchain, explainable AI, and incremental optimization, current survey literature studies each technology separately without considering how the four technologies can be harnessed to create synergies. A systematic review of 26 peer-reviewed studies published from 2018 to 2026 indicates that an integrated architecture incorporating federated learning, blockchain, explainable AI, and incremental optimization can be designed. This review identifies ten critical issues that need to be addressed when researching the four technologies. These issues include communication costs, scalability issues, interoperability concerns, limited clinical explainability, and high computational costs when applied in real-time situations. In comparison to privacy, scalability, interpretability, and efficiency, a hybrid approach can help improve data security, boost the interpretability of the models, facilitate data sharing, and prevent data-sharing risks. Overall quality assessment based on the CASP qualitative checklist analysis of all 26 studies indicated an average score of 7.0 out of 10, implying that the quality of the methods used in the studies was acceptable.