  Chest X-rays (CXRs) are the most frequently performed imaging test in
clinical practice. Recent advances in the development of vision-language
foundation models (FMs) give rise to the possibility of performing automated
CXR interpretation, which can assist physicians with clinical decision-making
and improve patient outcomes. However, developing FMs that can accurately
interpret CXRs is challenging due to the (1) limited availability of
large-scale vision-language datasets in the medical image domain, (2) lack of
vision and language encoders that can capture the complexities of medical data,
and (3) absence of evaluation frameworks for benchmarking the abilities of FMs
on CXR interpretation. In this work, we address these challenges by first
introducing \emph{CheXinstruct} - a large-scale instruction-tuning dataset
curated from 28 publicly-available datasets. We then present \emph{CheXagent} -
an instruction-tuned FM capable of analyzing and summarizing CXRs. To build
CheXagent, we design a clinical large language model (LLM) for parsing
radiology reports, a vision encoder for representing CXR images, and a network
to bridge the vision and language modalities. Finally, we introduce
\emph{CheXbench} - a novel benchmark designed to systematically evaluate FMs
across 8 clinically-relevant CXR interpretation tasks. Extensive quantitative
evaluations and qualitative reviews with five expert radiologists demonstrate
that CheXagent outperforms previously-developed general- and medical-domain FMs
on CheXbench tasks. Furthermore, in an effort to improve model transparency, we
perform a fairness evaluation across factors of sex, race and age to highlight
potential performance disparities. Our project is at
\url{https://stanford-aimi.github.io/chexagent.html}.
