AEGIS让医疗AI能安全持续更新，符合中美欧监管要求。
AEGIS: An Operational Infrastructure for Post-Market Governance of Adaptive Medical AI Under US and EU Regulations
- 构建三模块架构，实现医疗AI的动态治理与实时监控。
- 11次模拟中成功识别4类部署决策，提前发现模型失效风险。
- 适配不同临床场景，支持复杂医疗系统合规迭代。
部署于医疗器械中的机器学习系统需具备保障安全的同时支持持续改进的治理框架。美国FDA与欧盟已引入预定变更控制计划（PCCP）和上市后监测（PMS）机制，以避免重复申报即可进行模型迭代。本文提出AI/ML评估与安全治理基础设施（AEGIS），适用于任何医疗AI系统。AEGIS包含数据集成与重训练、模型监控、条件决策三个模块，实现FDA PCCP及欧盟《人工智能法案》第43(4)条要求。我们设计四类部署决策分类（批准、有条件批准、临床审查、拒绝），并引入独立的PMS警报信号，可在无可用可部署模型且当前发布模型同时失效时及时预警。通过脓毒症预测与脑肿瘤分割两个案例展示，两者采用相同治理架构，仅配置不同。在脓毒症案例的11次模拟中，AEGIS产生8次批准、1次有条件批准、1次临床审查、1次拒绝，涵盖全部四类决策；在第8与第10次迭代时触发联合警报，准确捕捉到关键失效状态。AEGIS在性能明显下降前即检测到漂移，验证其将监管变更控制理念转化为可执行治理流程的能力，支持多样临床应用下的医疗AI安全持续学习。
原文摘要 · Abstract (English)
Machine learning systems deployed in medical devices require governance frameworks that ensure safety while enabling continuous improvement. Regulatory bodies including the FDA and European Union have introduced mechanisms such as the Predetermined Change Control Plan (PCCP) and Post-Market Surveillance (PMS) to manage iterative model updates without repeated submissions. This paper presents AI/ML Evaluation and Governance Infrastructure for Safety (AEGIS), a governance framework applicable to any healthcare AI system. AEGIS comprises three modules, i.e., dataset assimilation and retraining, model monitoring, and conditional decision, that operationalize FDA PCCP and EU AI Act Article 43(4) provisions. We implement a four-category deployment decision taxonomy (APPROVE, CONDITIONAL APPROVAL, CLINICAL REVIEW, REJECT) with an independent PMS ALARM signal, enabling detection of the critical state in which no deployable model exists while the released model is simultaneously at risk. To illustrate how AEGIS can be instantiated across heterogeneous clinical contexts, we provide two examples: sepsis prediction from electronic health records and brain tumor segmentation from medical imaging. Both cases use identical governance architecture, differing only in configuration. Across 11 simulated iterations on the sepsis example, AEGIS yielded 8 APPROVE, 1 CONDITIONAL APPROVAL, 1 CLINICAL REVIEW, and 1 REJECT decision, exercising all four categories. ALARM signals were co-issued at iterations 8 and 10, including the critical state where no deployable model exists and the released model is simultaneously failing. AEGIS detected drift before observable performance degradation. These results demonstrate that AEGIS translates regulatory change-control concepts into executable governance procedures, supporting safe continuous learning for adaptive medical AI across diverse clinical applications.
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