Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant opportunity to streamline CXR interpretation and documentation. While foundation models are a promising solution, the lack of publicly available large-scale datasets and benchmarks inhibits their iterative development and real-world evaluation. To overcome these challenges, we constructed a large-scale dataset (CheXinstruct), which we utilized to train a vision-language foundation model (CheXagent). We systematically demonstrated competitive performance across eight distinct task types on our novel evaluation benchmark (CheXbench). Beyond technical validation, we assessed the real-world utility of CheXagent in directly drafting radiology reports. Our clinical assessment with eight radiologists revealed a 36% time saving for residents using CheXagent-drafted reports, while attending radiologists showed no significant time difference editing resident-drafted or CheXagent-drafted reports. The CheXagent-drafted reports improved the writing efficiency of both radiology residents and attending radiologists in 81% and 61% of cases, respectively, without loss of quality. Overall, we demonstrate that CheXagent can effectively perform a variety of CXR interpretation tasks and holds potential to assist radiologists in routine clinical workflows.
@article{arxiv.2401.12208,
title = {A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation},
author = {Zhihong Chen and Maya Varma and Justin Xu and Magdalini Paschali and Dave Van Veen and Andrew Johnston and Alaa Youssef and Louis Blankemeier and Christian Bluethgen and Stephan Altmayer and Jeya Maria Jose Valanarasu and Mohamed Siddig Eltayeb Muneer and Eduardo Pontes Reis and Joseph Paul Cohen and Cameron Olsen and Tanishq Mathew Abraham and Emily B. Tsai and Christopher F. Beaulieu and Jenia Jitsev and Sergios Gatidis and Jean-Benoit Delbrouck and Akshay S. Chaudhari and Curtis P. Langlotz},
journal= {arXiv preprint arXiv:2401.12208},
year = {2024}
}