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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. In this work, we present (i) \emph{CheXinstruct} - a large-scale instruction-tuning dataset curated from 28 publicly-available datasets; (ii) \emph{CheXagent} - an instruction-tuned FM capable of analyzing and summarizing CXRs; and (iii) \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 by up to 97.5%.