Title: Blockchain-Enabled Explainable AI for Trusted Healthcare Systems Show Chinese title

Title: 基于区块链的可解释人工智能用于可信医疗系统

Abstract: 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. Show Chinese abstract

Abstract: 本文介绍了一种区块链集成的可解释人工智能框架（BXHF），用于医疗系统，以解决健康信息网络面临的两个关键挑战：安全的数据交换和可理解的人工智能驱动的临床决策。 我们的架构结合了区块链，确保患者记录不可更改、可审计且防篡改，同时结合可解释人工智能（XAI）方法，产生透明且具有临床相关性的模型预测。 通过将安全保证和可解释性要求纳入统一的优化流程，BXHF确保数据层面的信任（通过验证和加密的记录共享）和决策层面的信任（通过可审计且符合临床的解释）。 其混合边缘-云架构允许不同机构之间的联邦计算，实现协作分析的同时保护患者隐私。 我们通过跨境临床研究网络、罕见疾病检测和高风险干预决策支持等用例展示了该框架的适用性。 通过确保透明度、可审计性和合规性，BXHF提高了人工智能在医疗领域的可信度、采用率和效果，为更安全、更可靠的临床决策奠定了基础。