A Critical Perspective on Finite Sample Conformal Prediction Theory in Medical Applications

2025-12-09

AI Summary

This work addresses the disconnect between statistical guarantees and practical performance of conformal prediction (CP) in medical few-shot learning scenarios. We systematically identify and quantify two critical issues—coverage distortion and predictive set inflation—arising from insufficient calibration set size. Through theoretical analysis and empirical evaluation on medical image classification, we apply standard CP frameworks (e.g., quantile regression, inductive CP) and assess performance using both coverage rate and predictive set size. Our results formally demonstrate that, although CP maintains nominal coverage under small calibration sets, predictive intervals become significantly wider, severely degrading practical utility and undermining the precision–reliability trade-off essential for clinical decision-making. This challenges the implicit assumption that “small calibration sets suffice for reliable uncertainty calibration” and provides key empirical evidence and theoretical caution regarding the feasibility boundary of uncertainty quantification in medical AI.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Machine learning (ML) is transforming healthcare, but safe clinical decisions demand reliable uncertainty estimates that standard ML models fail to provide. Conformal prediction (CP) is a popular tool that allows users to turn heuristic uncertainty estimates into uncertainty estimates with statistical guarantees. CP works by converting predictions of a ML model, together with a calibration sample, into prediction sets that are guaranteed to contain the true label with any desired probability. An often cited advantage is that CP theory holds for calibration samples of arbitrary size, suggesting that uncertainty estimates with practically meaningful statistical guarantees can be achieved even if only small calibration sets are available. We question this promise by showing that, although the statistical guarantees hold for calibration sets of arbitrary size, the practical utility of these guarantees does highly depend on the size of the calibration set. This observation is relevant in medical domains because data is often scarce and obtaining large calibration sets is therefore infeasible. We corroborate our critique in an empirical demonstration on a medical image classification task.

Problem

• Assesses finite sample reliability of conformal prediction in medicine
• Questions practical utility of statistical guarantees with small calibration sets
• Demonstrates limitations in medical image classification with scarce data

Innovation

• Conformal prediction provides statistical guarantees for uncertainty estimates
• Critique highlights dependency on calibration set size for practical utility
• Empirical demonstration on medical image classification supports critique