为医疗AI设计可解释性安全框架，让算法透明度成合规证据。
Embedding Explainable AI in NHS Clinical Safety: The Explainability-Enabled Clinical Safety Framework (ECSF)
- 将可解释性技术融入现有医疗软件安全标准流程
- 提出五个关键检查点，覆盖从风险识别到上市后监控
- 适合临床安全官、AI开发者及监管机构参考
人工智能正越来越多地嵌入英国国家医疗服务体系（NHS）工作流，但其概率性和自适应行为与现行临床安全标准所依赖的确定性假设相冲突。DCB0129和DCB0160虽为传统软件提供有力治理，却未明确AI特有的透明度、可解释性或模型漂移如何作为安全论证、隐患日志或上市后监测中的证据。本文提出可解释性增强型临床安全框架（ECSF），将可解释性整合进DCB0129/0160生命周期，使临床安全官可直接使用可解释性输出作为结构化安全证据，无需改变原有合规路径。通过跨监管映射，将DCB条款与良好机器学习实践（GMLP）、NHS AI保证与T.E.S.T.框架、欧盟人工智能法案的原则对齐，形成矩阵，关联监管条款、原则、ECSF检查点与适用的可解释性输出。ECSF设五项检查点：全局透明度用于隐患识别，病例级可解释性用于验证，临床医生可用性用于评估，可追溯决策路径用于风险控制，长期可解释性监控用于上市后监测。SHAP、LIME、集成梯度、显著性图、注意力可视化等技术被对应映射至相应DCB文档。该框架将可解释性重构为临床安全保障的核心要素，弥合确定性风险治理与AI概率行为之间的鸿沟，支持与GMLP、欧盟人工智能法案及NHS AI保证原则的协同。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is increasingly embedded in NHS workflows, but its probabilistic and adaptive behaviour conflicts with the deterministic assumptions underpinning existing clinical-safety standards. DCB0129 and DCB0160 provide strong governance for conventional software yet do not define how AI-specific transparency, interpretability, or model drift should be evidenced within Safety Cases, Hazard Logs, or post-market monitoring. This paper proposes an Explainability-Enabled Clinical Safety Framework (ECSF) that integrates explainability into the DCB0129/0160 lifecycle, enabling Clinical Safety Officers to use interpretability outputs as structured safety evidence without altering compliance pathways. A cross-regulatory synthesis mapped DCB clauses to principles from Good Machine Learning Practice, the NHS AI Assurance and T.E.S.T. frameworks, and the EU AI Act. The resulting matrix links regulatory clauses, principles, ECSF checkpoints, and suitable explainability outputs. ECSF introduces five checkpoints: global transparency for hazard identification, case-level interpretability for verification, clinician usability for evaluation, traceable decision pathways for risk control, and longitudinal interpretability monitoring for post-market surveillance. Techniques such as SHAP, LIME, Integrated Gradients, saliency mapping, and attention visualisation are mapped to corresponding DCB artefacts. ECSF reframes explainability as a core element of clinical-safety assurance, bridging deterministic risk governance with the probabilistic behaviour of AI and supporting alignment with GMLP, the EU AI Act, and NHS AI Assurance principles.
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