Predictive Performance Precision Analysis in Medicine: Identification of low-confidence predictions at patient and profile levels (MED3pa I)

ObjectiveArtificial Intelligence models are increasingly used in healthcare, yet global performance metrics can mask variations in reliability across individual patients or subgroups with shared attributes, called patient profiles. This study introduces MED3pa, a method that identifies when models are less reliable, allowing clinicians to better assess model limitations. Materials and MethodsWe propose a framework that estimates predictive confidence using three combined approaches: Individualized (IPC), Aggregated (APC), and Mixed Predictive Confidence (MPC). IPC estimates confidence for each patient, APC assesses it across profiles, and MPC combines both. We evaluate our method on four datasets: one simulated, two public, and one private clinical dataset. Metrics by Declaration Rate (MDR) curves show how performance changes when retaining only the most confident predictions, while interpretable decision trees reveal profiles with higher or lower model confidence. ResultsWe demonstrate our method in internal, temporal, and external validation settings, as well as through a clinical example. In internal validation, limiting predictions to the 93% most confident cases improved sensitivity by 14.3% and the AUC by 5.1%. In the clinical example, MED3pa identified a patient profile with high misclassification risk, demonstrating its potential for safer deployment. DiscussionBy identifying low-confidence predictions, our framework improves model reliability in clinical settings. It can be integrated into decision support systems to help clinicians make more informed decisions. Confidence thresholds help balance model performance with the proportion of patients for whom predictions are considered reliable. ConclusionBetter leveraging confidence in model predictions could improve reliability and trustworthiness, supporting safer and more effective use in healthcare.