Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models

Abstract

In this prognostic study, a proactive, label-agnostic monitoring pipeline detected harmful data shifts for a clinical AI system predicting in-hospital mortality. Transfer learning and drift-triggered continual learning strategies mitigated performance degradation, maintaining model performance across health care settings. These findings suggest that the approach used here may ensure the robust and equitable deployment of clinical AI models. Future research should explore the generalizability of this framework across diverse clinical domains, data modalities, and longer deployment periods to further validate its effectiveness.

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