CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation

Published: 29 Feb 2024, Last Modified: 01 Mar 2024AAAI 2024 SSS on Clinical FMsEveryoneRevisionsBibTeXCC BY 4.0
Track: Non-traditional track
Keywords: AI in health, Foundation Models, Chest X-rays
TL;DR: We design a complete scheme for developing CXR FMs by introducing CheXisntruct, CheXagent, and CheXbench, where experimental results demonstrate its effectiveness.
Abstract: 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\%.
Presentation And Attendance Policy: I have read and agree with the symposium's policy on behalf of myself and my co-authors.
Ethics Board Approval: No, our research does not involve datasets that need IRB approval or its equivalent.
Data And Code Availability: Yes, we will make data and code available upon acceptance.
Primary Area: Clinical foundation models
Student First Author: Yes, the primary author of the manuscript is a student.
Submission Number: 27
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