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.
@article{arxiv.2402.11217,
title = {A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models},
author = {Jie Liu and Wenxuan Wang and Yihang Su and Jingyuan Huan and Wenting Chen and Yudi Zhang and Cheng-Yi Li and Kao-Jung Chang and Xiaohan Xin and Linlin Shen and Michael R. Lyu},
journal= {arXiv preprint arXiv:2402.11217},
year = {2024}
}