Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare applications. Focusing on the domain of radiology, vision-language models (VLMs) achieve good performance results for tasks such as generating radiology findings based on a patient's medical image, or answering visual questions (e.g., 'Where are the nodules in this chest X-ray?'). However, the clinical utility of potential applications of these capabilities is currently underexplored. We engaged in an iterative, multidisciplinary design process to envision clinically relevant VLM interactions, and co-designed four VLM use concepts: Draft Report Generation, Augmented Report Review, Visual Search and Querying, and Patient Imaging History Highlights. We studied these concepts with 13 radiologists and clinicians who assessed the VLM concepts as valuable, yet articulated many design considerations. Reflecting on our findings, we discuss implications for integrating VLM capabilities in radiology, and for healthcare AI more generally.
@article{arxiv.2402.14252,
title = {Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology},
author = {Nur Yildirim and Hannah Richardson and Maria T. Wetscherek and Junaid Bajwa and Joseph Jacob and Mark A. Pinnock and Stephen Harris and Daniel Coelho de Castro and Shruthi Bannur and Stephanie L. Hyland and Pratik Ghosh and Mercy Ranjit and Kenza Bouzid and Anton Schwaighofer and Fernando Pérez-García and Harshita Sharma and Ozan Oktay and Matthew Lungren and Javier Alvarez-Valle and Aditya Nori and Anja Thieme},
journal= {arXiv preprint arXiv:2402.14252},
year = {2024}
}