Beyond P-Values: Importing Quantitative Finance's Risk and Regret Metrics for AI in Learning Health Systems
超越 P 值：将金融风险与遗憾指标引入学习型医疗系统的 AI 评价AI 解读 ↓
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AI 根据所提供的论文材料归类：研究对象是临床 AI 的证据与安全评价，借用金融风险概念不足以归入金融尾部或系统性风险研究。
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超越 P 值：将金融风险与遗憾指标引入学习型医疗系统的 AI 评价
- 研究问题
- 持续学习并处于非平稳环境的临床 AI，如何评价部署后的校准漂移、罕见严重损害与累积决策损失，补充传统显著性证据？
- 方法与思路
- 作者以观点文章形式借鉴量化金融风险理论和在线决策理论，提出用随时间变化的校准稳定性、下行风险界及累积遗憾评价系统。
- 材料陈述的贡献
- 摘要提出适应性医疗 AI 的证据评价框架，并明确将其作为随机试验与因果推断的补充，用于描述部署后才显现的风险维度。
- 阅读关注点
- 应核对风险、校准和遗憾指标的操作性定义、参照决策及评价时间尺度，并检查是否提供可实施程序和支持风险控制的证据。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要属于框架性倡议，未报告验证数据、具体风险界或临床安全改善结果。
原文摘要
The increasing deployment of artificial intelligence (AI) in clinical settings challenges foundational assumptions underlying traditional frameworks of medical evidence. Classical statistical approaches, centered on randomized controlled trials, frequentist hypothesis testing, and static confidence intervals, were designed for fixed interventions evaluated under stable conditions. In contrast, AI-driven clinical systems learn continuously, adapt their behavior over time, and operate in non-stationary environments shaped by evolving populations, practices, and feedback effects. In such systems, clinical harm arises less from average error rates than from calibration drift, rare but severe failures, and the accumulation of suboptimal decisions over time. In this perspective, we argue that prevailing notions of statistical significance are insufficient for characterizing evidence and safety in learning health systems. Drawing on risk-theoretic concepts from quantitative finance and online decision theory, we propose reframing medical evidence for adaptive AI systems in terms of time-indexed calibration stability, bounded downside risk, and controlled cumulative regret. We emphasize that this approach does not replace randomized trials or causal inference, but complements them by addressing dimensions of risk and uncertainty that emerge only after deployment. This framework provides a principled mathematical language for evaluating AI-driven clinical systems under continual learning and offers implications for clinical practice, research design, and regulatory oversight.