GP-VLS: A general-purpose vision language model for surgery

• URL: http://arxiv.org/abs/2407.19305v2
• Date: Tue, 6 Aug 2024 21:07:17 GMT
• Title: GP-VLS: A general-purpose vision language model for surgery
• Authors: Samuel Schmidgall, Joseph Cho, Cyril Zakka, William Hiesinger,
• Abstract summary: GP-VLS is a general-purpose vision language model for surgery. It integrates medical and surgical knowledge with visual scene understanding. We show GP-VLS significantly outperforms open- and closed-source models on surgical vision-language tasks.
• Score: 0.5249805590164902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Surgery requires comprehensive medical knowledge, visual assessment skills, and procedural expertise. While recent surgical AI models have focused on solving task-specific problems, there is a need for general-purpose systems that can understand surgical scenes and interact through natural language. This paper introduces GP-VLS, a general-purpose vision language model for surgery that integrates medical and surgical knowledge with visual scene understanding. For comprehensively evaluating general-purpose surgical models, we propose SurgiQual, which evaluates across medical and surgical knowledge benchmarks as well as surgical vision-language questions. To train GP-VLS, we develop six new datasets spanning medical knowledge, surgical textbooks, and vision-language pairs for tasks like phase recognition and tool identification. We show that GP-VLS significantly outperforms existing open- and closed-source models on surgical vision-language tasks, with 8-21% improvements in accuracy across SurgiQual benchmarks. GP-VLS also demonstrates strong performance on medical and surgical knowledge tests compared to open-source alternatives. Overall, GP-VLS provides an open-source foundation for developing AI assistants to support surgeons across a wide range of tasks and scenarios. The code and data for this work is publicly available at gpvls-surgery-vlm.github.io.

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