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

• URL: http://arxiv.org/abs/2402.11217v2
• Date: Fri, 29 Nov 2024 02:50:45 GMT
• Title: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models
• Authors: Jie Liu, Wenxuan Wang, Yihang Su, Jingyuan Huan, Wenting Chen, Yudi Zhang, Cheng-Yi Li, Kao-Jung Chang, Xiaohan Xin, Linlin Shen, Michael R. Lyu,
• Abstract summary: We introduce Asclepius, a novel Med-MLLM benchmark that assesses Med-MLLMs in terms of distinct medical specialties and different diagnostic capacities. Grounded in 3 proposed core principles, Asclepius ensures a comprehensive evaluation by encompassing 15 medical specialties. We also provide an in-depth analysis of 6 Med-MLLMs and compare them with 3 human specialists.
• Score: 57.88111980149541
• License:
• 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 complexity of real-world diagnostics across diverse specialties. To address this gap, we introduce Asclepius, a novel Med-MLLM benchmark that comprehensively assesses Med-MLLMs 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 overlap with existing VQA dataset. We further provide an in-depth analysis of 6 Med-MLLMs and compare them with 3 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.

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