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04/06/26

An ethics-informed computable audit framework for monitoring misdiagnosis risk in AI-assisted diagnosis.

Li, Yue; Yuan, MengYu; Yang, Yujing; Fu, Jia; Xin, Yi; Duan, Chu Jie; Wang, Jun. Sci Rep.

Collection Topics
AI and Diagnosis Measurement, Monitoring, and Event Analysis
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Summary

Diagnostic AI can fail when real-world patients diverge from training data. This study tested a real-time monitoring framework tracking data drift, fairness, calibration, and clinician-AI interactions via a composite health score and alerts. Simulations showed reliable detection of emerging issues, stability under normal conditions, and actionable oversight—offering a practical tool to monitor and audit diagnostic AI.

Read the paper
Li Y, Yuan M, Yang Y, et al. An ethics-informed computable audit framework for monitoring misdiagnosis risk in AI-assisted diagnosis. Sci Rep. 2026;16(1):16462.