Presentation : Scientific Days RSN
Date
2026-03-31
Authors
Summary
Olivier Lefebvre presented his research during the Réseau Santé Numérique (RSN) Scientific Day at Université Laval. He delivered a seven‑minute presentation highlighting the key contributions of his work and its implications for the development of reliable and trustworthy machine‑learning models in healthcare. The talk offered an accessible overview of his methodological advances and their relevance to real‑world clinical applications.
Abstract
Objectives Artificial Intelligence models are increasingly used in health care, yet global performance metrics can mask variations in reliability across individual patients or subgroups with shared attributes, called patient profiles. This study introduces predictive performance precision analysis in medicine (MED3pa), a method that identifies when models are less reliable, allowing clinicians to better assess model limitations.
Materials and Methods We propose a framework that estimates predictive confidence using 3 combined approaches: individualized (IPC), aggregated (APC), and mixed predictive confidence (MPC). Individualized predictive confidence estimates confidence for each patient, APC assesses it across profiles, and MPC combines both. We evaluate our method on 4 datasets: 1 simulated, 2 public, and 1 private clinical dataset. Metrics by declaration rate curves show how performance changes when retaining only the most confident predictions, while interpretable decision trees reveal profiles with higher or lower model confidence.
Results We 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 area under the receiver operating characteristic curve by 5.1%. In the clinical example, MED3pa identified a patient profile with high misclassification risk, demonstrating its potential for safer deployment.
Discussion By 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.
Conclusion Better leveraging confidence in model predictions could improve reliability and trustworthiness, supporting safer and more effective use in health care.