meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis

2025-12-19

AI Summary

This study addresses the lack of statistical rigor in evaluating medical AI models across patient- and imaging-based stratifications. We propose the first end-to-end subgroup analysis framework. Methodologically, it integrates Bayesian posterior estimation with bootstrap resampling to quantify metric uncertainty, applies the Benjamini–Hochberg procedure for multiplicity correction, and introduces an adaptive subgroup scanning algorithm to automatically detect statistically significant and clinically relevant performance disparities. The framework explicitly handles challenges including small-sample subgroups, base-rate imbalance, and intersectional subgroup testing. Evaluated on ISIC2020 (skin lesion classification) and MIMIC-CXR (chest radiograph diagnosis), it successfully identifies interpretable fairness-critical subgroups—e.g., demographic or anatomical cohorts exhibiting systematic underperformance—thereby substantially enhancing both the statistical reliability and clinical interpretability of model evaluation.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Analyzing machine learning model performance stratified by patient and recording properties is becoming the accepted norm and often yields crucial insights about important model failure modes. Performing such analyses in a statistically rigorous manner is non-trivial, however. Appropriate performance metrics must be selected that allow for valid comparisons between groups of different sample sizes and base rates; metric uncertainty must be determined and multiple comparisons be corrected for, in order to assess whether any observed differences may be purely due to chance; and in the case of intersectional analyses, mechanisms must be implemented to find the most `interesting' subgroups within combinatorially many subgroup combinations. We here present a statistical toolbox that addresses these challenges and enables practitioners to easily yet rigorously assess their models for potential subgroup performance disparities. While broadly applicable, the toolbox is specifically designed for medical imaging applications. The analyses provided by the toolbox are illustrated in two case studies, one in skin lesion malignancy classification on the ISIC2020 dataset and one in chest X-ray-based disease classification on the MIMIC-CXR dataset.

Problem

• Addresses statistically rigorous analysis of model performance across subgroups.
• Selects appropriate metrics for valid comparisons with varying sample sizes.
• Identifies interesting subgroups in intersectional analyses among many combinations.

Innovation

• Statistical toolbox for subgroup performance analysis
• Handles metric uncertainty and multiple comparisons
• Identifies interesting subgroups in intersectional analyses