ArticleNPJ digital medicine2025
EVA-X: a foundation model for general chest x-ray analysis with self-supervised learning.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice.Nature communications · 2026Article
- 3D foundation model for generalizable disease detection in head computed tomography.Nature biomedical engineering · 2026Article
- Foundation models for X-ray interpretation: a narrative review of current techniques and future perspectives in diagnostic imaging.Quantitative imaging in medicine and surgery · 2026Review
- Chest X-Ray Foundation Model With Global and Local Representations Integration.IEEE transactions on medical imaging · 2025Article
- Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis.Nature communications · 2025Article
- EchoFM: Foundation Model for Generalizable Echocardiogram Analysis.IEEE transactions on medical imaging · 2025Article
- URFM: A general Ultrasound Representation Foundation Model for advancing ultrasound image diagnosis.iScience · 2025Article
- Re-identification of patients from imaging features extracted by foundation models.NPJ digital medicine · 2025Article
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Authors and funding
8 authors.
Funding
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Abstract
Artificial intelligence analysis methods for chest X-ray images are limited by insufficient annotation data and varying levels of annotation, resulting in weak generalization ability and difficulty in clinical dissemination. Here, we present EVA-X, an innovative foundational model based on X-ray images with broad applicability. EVA-X uses a self-supervised learning method capable of capturing both semantic and geometric information from unlabeled images for universal X-ray image representation. It has demonstrated exceptional performance in chest disease analysis and localization, becoming a model capable of spanning over 20 different chest pathologies and achieving leading results in over 11 different pathology detection tasks. Additionally, EVA-X significantly reduces the burden of data annotation in the medical AI field, showcasing strong potential in the domain of few-shot learning. The emergence of EVA-X will greatly propel the development and application of foundational medical models, leading to potential improvements in future medical research and clinical practice.
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Registered trials
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