Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models

• URL: http://arxiv.org/abs/2402.11217v1
• Date: Sat, 17 Feb 2024 08:04:23 GMT
• Title: Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models
• Authors: Wenxuan Wang, Yihang Su, Jingyuan Huan, Jie Liu, Wenting Chen, Yudi Zhang, Cheng-Yi Li, Kao-Jung Chang, Xiaohan Xin, Linlin Shen, Michael R. Lyu
• Abstract summary: We introduce Asclepius, a novel benchmark for evaluating Medical Multi-Modal Large Language Models (Med-MLLMs) Asclepius rigorously and comprehensively assesses model capability in terms of distinct medical specialties and different diagnostic capacities. We also provide an in-depth analysis of 6 Med-MLLMs and compare them with 5 human specialists.
• Score: 59.60384461302662
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The significant breakthroughs of Medical Multi-Modal Large Language Models (Med-MLLMs) renovate modern healthcare with robust information synthesis and medical decision support. However, these models are often evaluated on benchmarks that are unsuitable for the Med-MLLMs due to the intricate nature of the real-world diagnostic frameworks, which encompass diverse medical specialties and involve complex clinical decisions. Moreover, these benchmarks are susceptible to data leakage, since Med-MLLMs are trained on large assemblies of publicly available data. Thus, an isolated and clinically representative benchmark is highly desirable for credible Med-MLLMs evaluation. To this end, we introduce Asclepius, a novel Med-MLLM benchmark that rigorously and comprehensively assesses model capability in terms of: distinct medical specialties (cardiovascular, gastroenterology, etc.) and different diagnostic capacities (perception, disease analysis, etc.). Grounded in 3 proposed core principles, Asclepius ensures a comprehensive evaluation by encompassing 15 medical specialties, stratifying into 3 main categories and 8 sub-categories of clinical tasks, and exempting from train-validate contamination. We further provide an in-depth analysis of 6 Med-MLLMs and compare them with 5 human specialists, providing insights into their competencies and limitations in various medical contexts. Our work not only advances the understanding of Med-MLLMs' capabilities but also sets a precedent for future evaluations and the safe deployment of these models in clinical environments. We launch and maintain a leaderboard for community assessment of Med-MLLM capabilities (https://asclepius-med.github.io/).

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