ArticleNature communications2025
Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.
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Who cites it
90 citing papers in PubMed.
- ProtoSurv: prototype-guided adaptation of computed tomography foundation model for lung-cancer prognosis prediction.Visual computing for industry, biomedicine, and art · 2026Article
- An Automated, Contamination-Controlled VQA Benchmark for Evaluating Vision-Language Models on 3D Oncology Imaging.Research square · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Article
- Deep learning-based PET/CT mixture-of-experts model for relapse risk stratification in relapsed/refractory classical hodgkin lymphoma: a multicenter study.European journal of nuclear medicine and molecular imaging · 2026Article
- Segment any tumour: an uncertainty-aware vision foundation model for whole-body analysis.Nature communications · 2026Article
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- A Bilingual Benchmark for Evaluating Diagnostic Performance of Multimodal Large Language Models in Radiology (RadM-Bench): Evaluation Development and Validation.Journal of medical Internet research · 2026Article
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Foundation models in biomedical imaging: turning hype into reality.Nature biomedical engineering · 2026Review
- From Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification.Journal of imaging informatics in medicine · 2026Article
- Why Only 3D for 3D? 2D-Guided Supervision for 3D Medical Vision-Language Models.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2026Article
- A multimodal vision-language model for comprehensive dental diagnosis and enhanced clinical practice.Nature communications · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Performance evaluation of domain-specific and general-purpose AI models for chest radiograph interpretation: a comparative study.BMC medical imaging · 2026Article
- Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.Scientific reports · 2026Article
- MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2026Article
- Do Multimodal Vision-Language Models Enhance the Medical Diagnostic Process? A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- A multi-modal foundation model for brain disease diagnosis and medical imaging.Patterns (New York, N.Y.) · 2026Article
- A disease-centric vision-language foundation model for precision oncology in kidney cancer.Nature communications · 2026Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, as a proof-of-concept, we aim to initiate the development of Radiology Foundation Model, termed as RadFM. We consider three perspectives: dataset construction, model design, and thorough evaluation, concluded as follows: (i), we contribute 4 multimodal datasets with 13M 2D images and 615K 3D scans. When combined with a vast collection of existing datasets, this forms our training dataset, termed as Medical Multi-modal Dataset, MedMD. (ii), we propose an architecture that enables to integrate text input with 2D or 3D medical scans, and generates responses for diverse radiologic tasks, including diagnosis, visual question answering, report generation, and rationale diagnosis; (iii), beyond evaluation on 9 existing datasets, we propose a new benchmark, RadBench, comprising three tasks aiming to assess foundation models comprehensively. We conduct both automatic and human evaluations on RadBench. RadFM outperforms former accessible multi-modal foundation models, including GPT-4V. Additionally, we adapt RadFM for diverse public benchmarks, surpassing various existing SOTAs.
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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.