aceEvaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance
Numerous measures have been proposed to illustrate the performance of predictive artificial intelligence (AI) models. Selecting appropriate performance measures is essential for predictive AI models intended for use in medical practice. Poorly performing models are misleading and may lead to wrong clinical decisions that can be detrimental to patients and increase financial costs. In this Viewpoint, we assess the merits of classic and contemporary performance measures when validating predictive AI models for med…
Appropriate evaluation using proper measures including AUROC, calibration plot, and net benefit with decision curve analysis is essential to validate predictive AI models that estimate binary outcome probabilities for medical practice.
Poorly performing predictive AI models are misleading and may lead to wrong clinical decisions that can be detrimental to patients and increase financial costs, with classification measures being improper at clinically relevant thresholds.
Guidance is scoped to models that estimate probabilities for a binary outcome, not multiclass, continuous, or other prediction tasks.
Evidence
- Peer-reviewedThe Lancet Digital Health2025-12-01
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Truvace Impact Record TRV-2026-0434, v1: “Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance.” Truvace, 2026-07-20. /record/TRV-2026-0434 (accessed at citation time). sha256 3185e1606a622eeb…
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