ReviewNPJ digital medicine2025
Distinct visual biases affect humans and artificial intelligence in medical imaging diagnoses.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Reliability and diagnostic performance of an automated MRI-based classifier compared with radiologists in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- The impact of arm position on image quality and radiation dose in pediatric toddler chest computed tomography: a phantom and clinical study.Frontiers in medicine · 2026Article
- The representational bottleneck in rehabilitation AI: from human-cognitive proxies to pathway-based clinical representations.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.