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<p>Artificial intelligence (AI) systems can detect subtle features in diagnostic imaging scans that radiologists may miss, including higher-order features that lack obvious visual correlates. This may enable earlier disease detection and non-invasive lesion phenotyping, but also introduces risks due to AI’s reliance on correlations rather than causation, potential demographic and technical biases, and uninterpretable reasoning. This perspective explores how radiologists and AI learn to perceive details in medical images differently, leading to potential discrepancies in medical decision-making.</p>

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Distinct visual biases affect humans and artificial intelligence in medical imaging diagnoses

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月22日

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  • Graham A. McLeod,
  • Emma A. M. Stanley,
  • Tom Rosenal,
  • Nils D. Forkert

摘要

Artificial intelligence (AI) systems can detect subtle features in diagnostic imaging scans that radiologists may miss, including higher-order features that lack obvious visual correlates. This may enable earlier disease detection and non-invasive lesion phenotyping, but also introduces risks due to AI’s reliance on correlations rather than causation, potential demographic and technical biases, and uninterpretable reasoning. This perspective explores how radiologists and AI learn to perceive details in medical images differently, leading to potential discrepancies in medical decision-making.