Distinct visual biases affect humans and artificial intelligence in medical imaging diagnoses.
expert_opinion · Level V
Where this comes from
- Record sourced from PubMed, PMID 41430423.
- Also identified by DOI 10.1038/s41746-025-02226-5 and PMC identifier 12820067.
- Licence recorded as CC BY-NC-ND.
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Abstract
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.