ArticleScientific reports2024
The risk of shortcutting in deep learning algorithms for medical imaging research.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Evaluating sex bias in artificial intelligence-based echocardiographic analysis: a retrospective study on individuals with normal cardiac function and patients with systolic and diastolic dysfunction.Cardiovascular ultrasound · 2026Article
- Artificial stupidity or logimorphism? How misuse of language warps our thinking about 'artificial intelligence'.European heart journal. Digital health · 2026Article
- AI-Based Detection of Osteoporosis on Dental Radiographs: Influence of Region-of-Interest Selection on Classification Performance.Journal of imaging · 2026Article
- The impact of tissue detection on diagnostic artificial intelligence algorithms in prostate digital pathology.Scientific reports · 2026Article
- Unsupervised Representation Learning Generates Differentiable Neurophysiological Profiles.bioRxiv : the preprint server for biology · 2026Article
- Mask of Truth: Model Sensitivity to Unexpected Regions of Medical Images.Journal of imaging informatics in medicine · 2026Article
- Cross-attention guided multi-modal network for breast ultrasound diagnosis incorporating objective clinical semantics.Frontiers in medicine · 2026Article
- Trail-blazing and keeping pace: building, retaining and expanding image analysis expertise.Frontiers in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While deep learning (DL) offers the compelling ability to detect details beyond human vision, its black-box nature makes it prone to misinterpretation. A key problem is algorithmic shortcutting, where DL models inform their predictions with patterns in the data that are easy to detect algorithmically but potentially misleading. Shortcutting makes it trivial to create models with surprisingly accurate predictions that lack all face validity. This case study shows how easily shortcut learning happens, its danger, how complex it can be, and how hard it is to counter. We use simple ResNet18 convolutional neural networks (CNN) to train models to do two things they should not be able to do: predict which patients avoid consuming refried beans or beer purely by examining their knee X-rays (AUC of 0.63 for refried beans and 0.73 for beer). We then show how these models' abilities are tied to several confounding and latent variables in the image. Moreover, the image features the models use to shortcut cannot merely be removed or adjusted through pre-processing. The end result is that we must raise the threshold for evaluating research using CNNs to proclaim new medical attributes that are present in medical images.
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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.