Defining operational safety in clinical artificial intelligence systems.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 41720960.
- Also identified by DOI 10.1038/s41746-026-02450-7 and PMC identifier 13050326.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
The clinical adoption of artificial intelligence (AI) has focused on enabling automation, but conventional accuracy metrics fail to answer a key question: when is it safe to trust an AI system? We introduce the Safety-Aware Receiver Operating Characteristic (SA-ROC) framework, which defines operational safety as an ability to meet pre-specified reliability levels. The SA-ROC curve delineates a Rule-in and a Rule-out Safe Zone, where autonomous action is permitted, and a Gray Zone, where human review is mandated. To quantify this non-automated workload, we introduce the Gray Zone Area (Γ<sub>Area</sub>), a metric measuring the operational cost of indecision. Our framework reveals a key reversal: in a case study of two FDA-cleared algorithms for cancer screening, the model with a statistically superior AUC was found to be operationally less safe for high-confidence screening. SA-ROC enables active governance, translating clinical policy into optimized workflows that inform operational safety and complement regulatory safety evaluation.