ArticleCell reports. Medicine2025
MetaGP: A generative foundation model integrating electronic health records and multimodal imaging for addressing unmet clinical needs.
Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness, Safety, and Workflow Burden of Large Language Model-Based Medical Report Generation: Systematic Review.Journal of medical Internet research · 2026Pooled it
- LungGPT: A unified multimodal system for interpretable diagnosis and clinical decision support of respiratory diseases.Cell reports. Medicine · 2026Article
- Large language models for pneumonia detection in radiology reports via text analysis.PLOS digital health · 2026Article
- Review
- Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.Patterns (New York, N.Y.) · 2026Review
- UniCAS: A foundation model for cervical cytology screening.Cell reports. Medicine · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
- Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.Health data science · 2026Review
- Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.Frontiers in medicine · 2026Review
- Artificial intelligence in emergency department triage: perspective of human professionals.Frontiers in digital health · 2025Article
- Generative AI in clinical (2020-2025): a mini-review of applications, emerging trends, and clinical challenges.Frontiers in digital health · 2025Review
- A systematic review of vision and vision-language foundation models in ophthalmology.Advances in ophthalmology practice and researchReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
25 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence makes strides in specialized diagnostics but faces challenges in complex clinical scenarios, such as rare disease diagnosis and emergency condition identification. To address these limitations, we develop Meta General Practitioner (MetaGP), a 32-billion-parameter generative foundation model trained on extensive datasets, including over 8 million electronic health records, biomedical literature, and medical textbooks. MetaGP demonstrates robust diagnostic capabilities, achieving accuracy comparable to experienced clinicians. In rare disease cases, it achieves an average diagnostic score of 1.57, surpassing GPT-4's 0.93. For emergency conditions, it improves diagnostic accuracy for junior and mid-level clinicians by 53% and 46%, respectively. MetaGP also excels in generating medical imaging reports, producing high-quality outputs for chest X-rays and computed tomography, often rated comparable to or superior to physician-authored reports. These findings highlight MetaGP's potential to transform clinical decision-making across diverse medical contexts.
Indexed as
Identifiers
What 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.