ArticleNature communications2026
Multimodal foundation models exploit text to make medical image predictions.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed.
- Advances in Multimodal Artificial Intelligence in Radiology: Data Integration, Foundation Models, and Clinical Applications-A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- A Rationale-Conditioned Image-Contrast Audit of OCT Dependence in Vision-Language Models for Anti-VEGF Treatment-Response Prediction.Bioengineering (Basel, Switzerland) · 2026Article
- Benchmarking Multimodal Vision Frontier Models With Lumbar Spine MRIs for Grading Lumbar Spinal Stenosis.Global spine journal · 2026Article
- A Longitudinal Analysis of the Usefulness, Readability, Consistency, and Capacity of Artificial Intelligence Chatbot Responses Regarding the Reality of Chronic Pain in Children.Paediatric & neonatal pain · 2026Article
- Reliability of Large Language Model Generated Clinical Reasoning in Assisted Reproductive Technology: Blinded Comparative Evaluation Study.Journal of medical Internet research · 2026Article
- Will your next surgeon be a robot? Autonomy and AI in robotic surgery.Science robotics · 2025Review
- MicroscopyGPT: Generating Atomic-Structure Captions from Microscopy Images of 2D Materials with Vision-Language Transformers.The journal of physical chemistry letters · 2025Article
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- Article
- Employing large language models safely and effectively as a practicing neurosurgeon.Acta neurochirurgica · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Multimodal foundation models have shown compelling but conflicting performance in medical image interpretation. However, the ways in which these models integrate and prioritize different data modalities, including images and text, remain poorly understood. Here we evaluate 8 proprietary and open-source multimodal foundation models using 1090 multimodal medical cases. We show that image predictions are largely driven by text, with accuracy increasing monotonically with the amount of informative text. Exploitation of text is a double-edged sword; even mild suggestions of an incorrect diagnosis in text diminish image-based classification, dramatically reducing performance in cases the model could previously answer using images alone-o3 accuracy fell from 84% to 28% when a misleading clinical vignette was introduced. In physician evaluations of long-form cases, adding images reduces or does not improve performance when text is highly informative (e.g., GPT-4V showed decreased accuracy when images were added to highly informative text across 69 clinicopathological conferences). Our results suggest that multimodal AI models may be useful in medical diagnostic reasoning but that their accuracy is largely driven, for better and worse, by text.
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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.