ArticleNPJ digital medicine2025
A multimodal multidomain multilingual medical foundation model for zero shot clinical diagnosis.
Article in NPJ digital medicine, 2025. 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 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.
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Who cites it
11 citing papers in PubMed.
- 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
- Review
- Translating multimodal foundation models into oncology: Toward a future where AI directs diagnosis and therapy.Genes & diseases · 2026Article
- BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.Nature biomedical engineering · 2026Article
- Multimodal deep learning with missing data robustness for enhanced early diagnosis of coronary artery disease using CCTA, clinical, and ECG data.BMC medical informatics and decision making · 2026Article
- UltraReporter for transforming spoken diagnostic cues into structured ultrasound reports with large language models.Scientific reports · 2026Article
- Multimodal-multiscale hybrid fusion network for automated esophageal cancer diagnosis.iScience · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Oral microbiome dysbiosis in autism spectrum disorder: the oral-gut-brain axis and future perspectives: a narrative review.Frontiers in microbiology · 2026Review
- Efficient and accurate medical AI: MediLore and MediOut.Frontiers in artificial intelligence · 2026Article
- Keyword-based AI assistance in the generation of radiology reports: A pilot study.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiology images are one of the most commonly used in daily clinical diagnosis. Typically, clinical diagnosis using radiology images involves disease reporting and classification, where the former is a multimodal task whereby textual reports are generated to describe clinical findings in images, as are common in various domains, e.g., chest X-ray or computed tomography. Existing approaches are mainly supervised, the quality of which heavily depends on the volume and quality of available labeled data. However, for rarer or more novel diseases, enrolling patients to collect data is both time-consuming and expensive. For non-English languages, sufficient quantities of labeled data are typically not available. We propose the Multimodal Multidomain Multilingual Foundation Model. It is useful for rare diseases and non-English languages, where the labeled data are frequently much more scarce, and may even be absent. Our approach achieves encouraging performances on nine datasets, including 2 infectious and 14 non-infectious diseases.
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.