  The diagnosis and treatment of chest diseases play a crucial role in
maintaining human health. X-ray examination has become the most common clinical
examination means due to its efficiency and cost-effectiveness. 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 to various chest disease detection tasks. EVA-X is the
first X-ray image based self-supervised learning method capable of capturing
both semantic and geometric information from unlabeled images for universal
X-ray image representation. Through extensive experimentation, EVA-X has
demonstrated exceptional performance in chest disease analysis and
localization, becoming the first model capable of spanning over 20 different
chest diseases and achieving leading results in over 11 different detection
tasks in the medical field. 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, bringing about
revolutionary changes in future medical research and clinical practice. Our
codes and models are available at: https://github.com/hustvl/EVA-X.
