患病率漂移下共形分诊中发布侧风险的部署审计 / A Deployment Audit of Release-Side Risk in Conformal Triage under Prevalence Shift
1️⃣ 一句话总结
本文提出了一种针对AI分诊系统的部署审计方法,用于检测当疾病实际发生率变化时,系统是否会错误地将真正患病的患者直接“放行”而未经过人工审查,从而避免虚假的安全感。
Conformal triage converts predictive scores into deployment actions that either release a case, flag it for urgent attention, or defer it to human review. Under prevalence shift, however, the usual summaries of marginal coverage and human-review rate can miss the safety-critical question of whether patients who truly experience the target event are released without review. To address this gap, we introduce a leakage-aware deployment audit for release-side conformal triage. It first assigns target subjects to three non-overlapping roles: prevalence correction, conformal calibration, and held-out release-safety evaluation. This separation then lets the audit evaluate release directly: how many event-positive patients are cleared without review, whether the pilot has enough event labels for calibration, and how the safety-review trade-off shifts. Applying this audit to a retrospective NSCLC pilot shows why lower review can be misleading: after prevalence correction, the pooled conformal branch lowers review by releasing more patients, some of whom are event-positive. Within the audit, the classwise branch acts as a scarcity diagnostic: the pilot has too few event labels to certify safe low-review release.
患病率漂移下共形分诊中发布侧风险的部署审计 / A Deployment Audit of Release-Side Risk in Conformal Triage under Prevalence Shift
本文提出了一种针对AI分诊系统的部署审计方法,用于检测当疾病实际发生率变化时,系统是否会错误地将真正患病的患者直接“放行”而未经过人工审查,从而避免虚假的安全感。
源自 arXiv: 2605.20956