Blockchain-Enabled Explainable AI for Trusted Healthcare Systems
It addresses the problem of building trusted AI systems for healthcare stakeholders, but it appears incremental as it combines existing technologies like blockchain and XAI without a fundamentally new approach.
This paper tackles the challenges of secure data exchange and interpretable AI-driven clinical decisions in healthcare by introducing a Blockchain-Integrated Explainable AI Framework (BXHF), which integrates blockchain for immutable records and XAI for transparent predictions to enhance trust and effectiveness in clinical applications.
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.