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June 8, 2026npj Artificial IntelligenceOpen Access

Evidence over explanations: put medical AI to the test

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Authors

FPFilippo PesapaneARAnna RotiliSPSilvia Penco

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Overview

Randomized trial advocates testing medical AI for causal alignment, indicating the need for better validation methods.

Key Points

  • The research aims to improve the reliability of medical AI by advocating for empirical tests of causal alignment and governance through testability.
  • Proposing preregistered empirical trials for testing medical AI models
  • Advocating for governance structures like institutional AI with audit trails
  • Suggesting external audits and evaluations similar to those for large language models (LLMs)
  • Accentuates the inadequacy of existing XAI and interpretable models in practical settings
  • Suggests that without proper testability, medical AI poses risks for justified clinical claims
  • Indicates a need for systematic real-world testing across diverse conditions and populations

Cite This Study

Pesapane et al. (2026) studied this question.

synapsesocial.com/papers/6a265bb6ad53cfb9357c52d6https://doi.org/10.1038/s44387-026-00092-4
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