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June 3, 2026Artificial Intelligence in Medicine2 citationsOpen Access

A critical perspective on finite sample conformal prediction theory in medical applications

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KKKlaus-Rudolf KladnyMax Planck Institute for Developmental BiologyBSBernhard SchölkopfBernstein Center for Computational Neuroscience TübingenLKLisa KochUniversity Hospital of Bern

Key Points

  • The study critically evaluates the validity of finite sample conformal prediction theory in medical contexts, particularly focusing on calibration issues.
  • Analyzed the performance of marginal conformal coverage under various calibration workflows.
  • Examined histology classification to assess under-coverage linked to small calibration sets.
  • Identified the need for larger calibration sets to improve conditional guarantees.
  • Marginal conformal coverage often misleads when using one-time calibration workflows.
  • Limited calibration data resulted in significant variability in conditional coverage rates.
  • Frequent under-coverage was observed in histology classification with small calibration sets.

Abstract

• Marginal conformal coverage can mislead under one-time calibration workflows. • Small calibration sets yield large variability in conditional coverage. • Histology classification shows frequent under-coverage with limited calibration data. • Conditional guarantees need much larger calibration sets to be informative. • Implications for safe deployment and regulation of medical machine learning.

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Cite This Study

Kladny et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc756dee9eb8c0dce83cchttps://doi.org/10.1016/j.artmed.2026.103462
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A critical perspective on finite sample conformal prediction theory in medical applications.2026
  2. 2Probabilistic Conformal Coverage Guarantees in Small-Data Settings2025
  3. 3Coverage Fairness in Conformal Prediction Under Class Imbalance: When Class-Conditional Calibration Helps and When It Backfires2026
  4. 4Class-Conditional Conformal Prediction for Reliable Anomaly Detection Under Extreme Class Imbalance2026
  5. 5Conformal prediction without knowledge of labeled calibration data2025