A fully open AI foundation model applied to chest radiography
成果类型：
Article
署名作者：
Ma, Dongao; Pang, Jiaxuan; Gotway, Michael B.; Liang, Jianming
署名单位：
Arizona State University; Arizona State University-Tempe; Mayo Clinic; Mayo Clinic Phoenix; Arizona State University; Arizona State University-Downtown Phoenix
刊物名称：
Nature
ISSN/ISSBN：
0028-1589
DOI：
10.1038/s41586-025-09079-8
发表日期：
2025-07-10
关键词：
摘要：
Chest radiography frequently serves as baseline imaging for most lung diseases1. Deep learning has great potential for automating the interpretation of chest radiography2. However, existing chest radiographic deep learning models are limited in diagnostic scope, generalizability, adaptability, robustness and extensibility. To overcome these limitations, we have developed Ark+, a foundation model applied to chest radiography and pretrained by cyclically accruing and reusing the knowledge from heterogeneous expert labels in numerous datasets. Ark+ excels in diagnosing thoracic diseases. It expands the diagnostic scope and addresses potential misdiagnosis. It can adapt to evolving diagnostic needs and respond to novel diseases. It can learn rare conditions from a few samples and transfer to new diagnostic settings without training. It tolerates data biases and long-tailed distributions, and it supports federated learning to preserve privacy. All codes and pretrained models have been released, so that Ark+ is open for fine-tuning, local adaptation and improvement. It is extensible to several modalities. Thus, it is a foundation model for medical imaging. The exceptional capabilities of Ark+ stem from our insight: aggregating various datasets diversifies the patient populations and accrues knowledge from many experts to yield unprecedented performance while reducing annotation costs3. The development of Ark+ reveals that open models trained by accruing and reusing knowledge from heterogeneous expert annotations with a multitude of public (big or small) datasets can surpass the performance of proprietary models trained on large data. We hope that our findings will inspire more researchers to share code and datasets or federate privacy-preserving data to create open foundation models with diverse, global expertise and patient populations, thus accelerating open science and democratizing AI for medicine.