ArticleNature medicine2024
Transparent medical image AI via an image-text foundation model grounded in medical literature.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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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.
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Who cites it
44 citing papers in PubMed.
- AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial.Nature medicine · 2026Trial
- Artificial intelligence in central nervous system autoimmune diseases: from clinical decision support to mechanistic discovery and translation.EMBO molecular medicine · 2026Review
- Beyond Pattern Recognition: Call for Functionally Aware AI for Anatomical Illustration.JMIR medical education · 2026Article
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay people.Nature medicine · 2026Article
- CLEAR: an auditable foundation model for radiology grounded in clinical concepts.Nature biomedical engineering · 2026Article
- Dissecting and directing pathology foundation models.bioRxiv : the preprint server for biology · 2026Article
- A generalist biomedical vision-language model via multi-CLIP knowledge distillation.Nature communications · 2026Article
- Agentic AI in global health equity for high altitude populations.NPJ digital medicine · 2026Review
- A universal foundation model for grounded biomedical image interpretation.Nature communications · 2026Article
- PRISM: a clinically interpretable stepwise framework for multimodal skin cancer diagnosis.Scientific reports · 2026Article
- Bridging the interpretability gap for medical artificial intelligence models using class-association manifold learning.Nature biomedical engineering · 2026Article
- PatchCLIP enables region specific contrastive health record and image joint training with patch embedding loss.Scientific reports · 2026Article
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Review
- Domain specific multimodal large language model for automated endoscopy reporting with multicenter prospective validation.NPJ digital medicine · 2026Article
- An ophthalmic video foundation model for surgical recognition and navigation with wet-lab porcine eye validation.Nature biomedical engineering · 2026Article
- An interpretable AI system reduces false-positive MRI diagnoses by stratifying high-risk breast lesions.Nature communications · 2026Article
- Mask of Truth: Model Sensitivity to Unexpected Regions of Medical Images.Journal of imaging informatics in medicine · 2026Article
- Reducing bulky medical images via shape-texture decoupled deep neural networks.Nature communications · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones and imaging modalities. We trained MONET based on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with supervised models built on previously concept-annotated dermatology datasets of clinical images. We demonstrate how MONET enables AI transparency across the entire AI system development pipeline, from building inherently interpretable models to dataset and model auditing, including a case study dissecting the results of an AI clinical trial.
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38627560What OpenQuestion holds
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.