@inproceedings{liu-etal-2025-asclepius,
title = "Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models",
author = "Liu, Jie and
Wang, Wenxuan and
Yihang, Su and
Huang, Jingyuan and
Zhang, Yudi and
Li, Cheng-Yi and
Chen, Wenting and
Xing, Xiaohan and
Chang, Kao-Jung and
Shen, Linlin and
Lyu, Michael R.",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1178/",
doi = "10.18653/v1/2025.acl-long.1178",
pages = "24181--24201",
ISBN = "979-8-89176-251-0",
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. Thus, a clinically representative benchmark is highly desirable for credible Med-MLLMs evaluation. To this end, 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 the 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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<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. Thus, a clinically representative benchmark is highly desirable for credible Med-MLLMs evaluation. To this end, 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 the 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.</abstract>
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%0 Conference Proceedings
%T Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models
%A Liu, Jie
%A Wang, Wenxuan
%A Yihang, Su
%A Huang, Jingyuan
%A Zhang, Yudi
%A Li, Cheng-Yi
%A Chen, Wenting
%A Xing, Xiaohan
%A Chang, Kao-Jung
%A Shen, Linlin
%A Lyu, Michael R.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F liu-etal-2025-asclepius
%X 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. Thus, a clinically representative benchmark is highly desirable for credible Med-MLLMs evaluation. To this end, 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 the 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.
%R 10.18653/v1/2025.acl-long.1178
%U https://aclanthology.org/2025.acl-long.1178/
%U https://doi.org/10.18653/v1/2025.acl-long.1178
%P 24181-24201
Markdown (Informal)
[Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models](https://aclanthology.org/2025.acl-long.1178/) (Liu et al., ACL 2025)
ACL
- Jie Liu, Wenxuan Wang, Su Yihang, Jingyuan Huang, Yudi Zhang, Cheng-Yi Li, Wenting Chen, Xiaohan Xing, Kao-Jung Chang, Linlin Shen, and Michael R. Lyu. 2025. Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 24181–24201, Vienna, Austria. Association for Computational Linguistics.