Blockchain-Enabled Explainable AI for Trusted Healthcare Systems
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911161826410496 |
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| author | Mohsin, Md Talha |
| author_facet | Mohsin, Md Talha |
| contents | This paper introduces a Blockchain-Integrated Explainable AI Framework (BXHF) for healthcare systems to tackle two essential challenges confronting health information networks: safe data exchange and comprehensible AI-driven clinical decision-making. Our architecture incorporates blockchain, ensuring patient records are immutable, auditable, and tamper-proof, alongside Explainable AI (XAI) methodologies that yield transparent and clinically relevant model predictions. By incorporating security assurances and interpretability requirements into a unified optimization pipeline, BXHF ensures both data-level trust (by verified and encrypted record sharing) and decision-level trust (with auditable and clinically aligned explanations). Its hybrid edge-cloud architecture allows for federated computation across different institutions, enabling collaborative analytics while protecting patient privacy. We demonstrate the framework's applicability through use cases such as cross-border clinical research networks, uncommon illness detection and high-risk intervention decision support. By ensuring transparency, auditability, and regulatory compliance, BXHF improves the credibility, uptake, and effectiveness of AI in healthcare, laying the groundwork for safer and more reliable clinical decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14987 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Blockchain-Enabled Explainable AI for Trusted Healthcare Systems Mohsin, Md Talha Cryptography and Security Artificial Intelligence Machine Learning This paper introduces a Blockchain-Integrated Explainable AI Framework (BXHF) for healthcare systems to tackle two essential challenges confronting health information networks: safe data exchange and comprehensible AI-driven clinical decision-making. Our architecture incorporates blockchain, ensuring patient records are immutable, auditable, and tamper-proof, alongside Explainable AI (XAI) methodologies that yield transparent and clinically relevant model predictions. By incorporating security assurances and interpretability requirements into a unified optimization pipeline, BXHF ensures both data-level trust (by verified and encrypted record sharing) and decision-level trust (with auditable and clinically aligned explanations). Its hybrid edge-cloud architecture allows for federated computation across different institutions, enabling collaborative analytics while protecting patient privacy. We demonstrate the framework's applicability through use cases such as cross-border clinical research networks, uncommon illness detection and high-risk intervention decision support. By ensuring transparency, auditability, and regulatory compliance, BXHF improves the credibility, uptake, and effectiveness of AI in healthcare, laying the groundwork for safer and more reliable clinical decision-making. |
| title | Blockchain-Enabled Explainable AI for Trusted Healthcare Systems |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.14987 |