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Calibration-informed metrics for instance-level predictive reliability in medical AI.

Federico Cabitza1

  • 1Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, Milano, 20126, Italy; Digital Health & Wellbeing Center, Fondazione Bruno Kessler (FBK), Via Sommarive, 18, Povo, Trento, 38123, Italy.

Artificial Intelligence in Medicine
|February 8, 2026
PubMed
Summary
This summary is machine-generated.

New metrics, Local Predictive Value (LPV) and Credible Predictive Value (CPV), assess individual AI prediction reliability in healthcare. These metrics enhance trust in clinical decision support systems by providing interpretable reliability estimates.

Keywords:
CalibrationConfidence intervalCredible intervalsMedical MLTransparent AI

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Medical AI Reliability

Background:

  • Traditional performance metrics (e.g., accuracy, sensitivity) in clinical decision support systems (CDSS) do not adequately convey the reliability of individual predictions.
  • Clinicians require reliable individual predictions, especially in high-stakes medical environments.

Purpose of the Study:

  • To introduce a novel calibration-informed framework with two new metrics: Local Predictive Value (LPV) and Credible Predictive Value (CPV).
  • To provide interpretable and trustworthy reliability estimates for individual AI predictions in medical applications.

Main Methods:

  • LPV estimates prediction reliability by analyzing correctness frequency within confidence score neighborhoods.
  • CPV refines LPV using a Bayesian approach, incorporating global predictive values as priors for a posterior correctness probability distribution.
  • The framework was applied to benchmark medical imaging datasets.

Main Results:

  • LPV and CPV generated locally adaptive and interpretable reliability estimates for AI predictions.
  • The metrics successfully identified instances where local evidence was insufficient or misleading.
  • Bayesian smoothing in CPV demonstrated improved stability against sparse or misleading local data.

Conclusions:

  • The LPV and CPV framework enhances the interpretability and trustworthiness of medical AI systems at the individual case level.
  • Combining local calibration with Bayesian inference offers a robust approach to assessing AI prediction reliability.
  • These metrics are crucial for advancing the development of reliable AI in healthcare.