用区块链+可解释AI构建可信医疗系统，保障数据安全与决策透明。
Blockchain-Enabled Explainable AI for Trusted Healthcare Systems
- 融合区块链与可解释AI，实现医疗数据不可篡改与模型可理解。
- 支持跨机构联邦计算，保护隐私的同时完成协作分析。
- 适用于跨境研究、罕见病诊断等高风险医疗场景，提升可信度。
本文提出一种区块链集成的可解释AI框架（BXHF），解决医疗信息网络中的两大挑战：安全的数据交换与可理解的AI临床决策。该架构结合区块链技术，确保患者记录不可更改、可审计且防篡改，同时采用可解释AI（XAI）方法生成透明且符合临床意义的预测结果。通过将安全性和可解释性整合到统一优化流程中，BXHF实现了数据级信任（经验证与加密共享）和决策级信任（可审计且临床对齐的解释）。其混合边缘-云架构支持跨机构联邦计算，促进协作分析的同时保护患者隐私。我们在跨境临床研究网络、罕见病检测及高风险干预决策支持等场景中验证了框架的适用性。通过保障透明性、可审计性与合规性，BXHF提升了AI在医疗中的可信度、采纳率与有效性，为更安全可靠的临床决策奠定基础。
原文摘要 · Abstract (English)
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.
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