ArticleNature biomedical engineering2025
Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.Cancer research · 2026Article
- Large reasoning models as thinking machines for medicine.Nature biomedical engineering · 2026Review
- CLEAR: an auditable foundation model for radiology grounded in clinical concepts.Nature biomedical engineering · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Attention guided fair artificial intelligence modeling for skin cancer diagnosis.NPJ digital medicine · 2026Article
- Dissecting and directing pathology foundation models.bioRxiv : the preprint server for biology · 2026Article
- Clinically Explainable Disease Diagnosis Based on Biomarker Activation Map.IEEE transactions on bio-medical engineering · 2026Article
- Artificial Intelligence in Label-Free Optical Imaging Applications.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Transparency of medical artificial intelligence systems.Nature reviews bioengineering · 2026Article
- Large-scale generative tumor synthesis in computed tomography images for improving tumor recognition.Nature communications · 2025Article
- Preoperative risk prediction of major cardiovascular events in noncardiac surgery using the 12-lead electrocardiogram: an explainable deep learning approach.British journal of anaesthesia · 2025Observational
- Interpretable framework for predicting preoperative cardiorespiratory fitness using wearable data.Computer methods and programs in biomedicine · 2025Article
- Ethical considerations and robustness of artificial neural networks in medical image analysis under data corruption.Scientific reports · 2025Article
- Counterfactual Diffusion Models for Interpretable Morphology-based Explanations of Artificial Intelligence Models in Pathology.bioRxiv : the preprint server for biology · 2025Article
- DREAM: A framework for discovering mechanisms underlying AI prediction of protected attributes.medRxiv : the preprint server for health sciences · 2025Article
- Digital twins as global learning health and disease models for preventive and personalized medicine.Genome medicine · 2025Review
- Representative training data sets are critical for accurate machine-learning classification of microscopy images of particles formed by lipase-catalyzed polysorbate hydrolysis.Journal of pharmaceutical sciences · 2025Article
- Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics.Computational and structural biotechnology journal · 2025Review
- Evaluating Sex and Age Biases in Multimodal Large Language Models for Skin Disease Identification from Dermatoscopic Images.Health data science · 2025Article
- Machine learning methods for histopathological image analysis: Updates in 2024.Computational and structural biotechnology journal · 2025Review
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Authors and funding
5 authors.
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Abstract
The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from melanoma 'lookalikes' on the basis of dermoscopic and clinical images of the skin, and the power of generative models to render 'counterfactual' images to understand the 'reasoning' processes of five medical-image classifiers. By altering image attributes to produce analogous images that elicit a different prediction by the classifiers, and by asking physicians to identify medically meaningful features in the images, the counterfactual images revealed that the classifiers rely both on features used by human dermatologists, such as lesional pigmentation patterns, and on undesirable features, such as background skin texture and colour balance. The framework can be applied to any specialized medical domain to make the powerful inference processes of machine-learning models medically understandable.
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