ArticleNature medicine2025
Self-improving generative foundation model for synthetic medical image generation and clinical applications.
Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The future of Alzheimer's disease risk prediction: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- The spectrum of radiation therapy options for craniopharyngioma: a systematic review.Journal of neuro-oncology · 2025Pooled it
- Bridging scales: a perspective on macro-to-micro transfer for microscopy foundation models.Npj imaging · 2026Article
- FFA-SegGPT: A Two-Stage Cross-Modal AI System to Optimize the Clinical Workflow of Diabetic Retinopathy FFA Interpretation.Translational vision science & technology · 2026Article
- Ten years, tens of millions of cells.Nature biotechnology · 2026Article
- Synthetic Echocardiograms from Diffusion Models in Rare Cardiovascular Disease.medRxiv : the preprint server for health sciences · 2026Article
- Longitudinal alignments and syntheses of multimodal clinical data for personalized medicine with the PULSE framework.Nature computational science · 2026Article
- Foundation models in medical image analysis: A systematic review and quantitative analysis.Medical image analysis · 2026Review
- A foundation model for acute abdomen diagnosis stratification and triage on noncontrast computed tomography.Nature communications · 2026Article
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Foundation models in biomedical imaging: turning hype into reality.Nature biomedical engineering · 2026Review
- Age outweighs route for AAV-PHP.eB transgene distribution via inner ear delivery.Drug delivery and translational research · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- A Systematic Review on Synthetic Medical Images Generation-Recent Trends and Future Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- Ethical and Governance Challenges of AI in Medical Imaging and Diagnostics: A Systematic Survey and Policy Framework Recommendations.Healthcare (Basel, Switzerland) · 2026Review
- Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Integrating Anatomical Priors Into a Causal Diffusion Model.IEEE transactions on medical imaging · 2026Article
- Towards clinical-level interpretation of dental panoramic radiography using an instance-guided vision-language model.Nature biomedical engineering · 2026Article
- FairGen: preference-aligned diffusion for demographically equitable medical image synthesis.NPJ digital medicine · 2026Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
36 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In many clinical and research settings, the scarcity of high-quality medical imaging datasets has hampered the potential of artificial intelligence (AI) clinical applications. This issue is particularly pronounced in less common conditions, underrepresented populations and emerging imaging modalities, where the availability of diverse and comprehensive datasets is often inadequate. To address this challenge, we introduce a unified medical image-text generative model called MINIM that is capable of synthesizing medical images of various organs across various imaging modalities based on textual instructions. Clinician evaluations and rigorous objective measurements validate the high quality of MINIM's synthetic images. MINIM exhibits an enhanced generative capability when presented with previously unseen data domains, demonstrating its potential as a generalist medical AI (GMAI). Our findings show that MINIM's synthetic images effectively augment existing datasets, boosting performance across multiple medical applications such as diagnostics, report generation and self-supervised learning. On average, MINIM enhances performance by 12% for ophthalmic, 15% for chest, 13% for brain and 17% for breast-related tasks. Furthermore, we demonstrate MINIM's potential clinical utility in the accurate prediction of HER2-positive breast cancer from MRI images. Using a large retrospective simulation analysis, we demonstrate MINIM's clinical potential by accurately identifying targeted therapy-sensitive EGFR mutations using lung cancer computed tomography images, which could potentially lead to improved 5-year survival rates. Although these results are promising, further validation and refinement in more diverse and prospective settings would greatly enhance the model's generalizability and robustness.
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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.