@inproceedings{wei-etal-2026-medreamm,
title = "{M}ed{R}ea{MM}: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis",
author = "Wei, Lai and
Chen, Yuchao and
Cao, Zhenbiao and
Zhang, Xiaojin and
Wei, Zhongyu and
Wang, Bangting and
Chen, Wei and
Bai, Xiang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.881/",
pages = "18590--18618",
ISBN = "979-8-89176-462-0",
abstract = "The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus on textual reasoning or isolated visual question-answering (VQA) tasks, lacking holistic integration of clinical narratives and medical imaging, and thus failing to assess the multimodal diagnostic synthesis capability central to expert clinical judgment. To bridge this gap, we introduce **MedReaMM**, a benchmark specifically designed to evaluate models' ability to synthesize heterogeneous clinical evidence consisting of detailed patient histories alongside multiple medical images into accurate differential diagnoses under a complete-information paradigm. Constructed from case reports sourced from top-tier medical journals and curated clinical case databases, MedReaMM comprises 625 expert-validated cases with an average of 2.79 medical images per case and a total of 1,042 standardized diagnoses annotated with ICD-11 codes. These cases predominantly represent rare, atypical, or multi-system presentations that demand expert-level evidence integration beyond routine pattern recognition. We evaluate 23 Large Multimodal Models (LMMs) and find that most achieve diagnostic accuracy scores below 50{\%}, underscoring a substantial gap in multimodal diagnostic synthesis capability. Further analysis reveals that medical knowledge proficiency, medical image understanding, and evidence integration are all highly correlated with diagnostic performance."
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<abstract>The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus on textual reasoning or isolated visual question-answering (VQA) tasks, lacking holistic integration of clinical narratives and medical imaging, and thus failing to assess the multimodal diagnostic synthesis capability central to expert clinical judgment. To bridge this gap, we introduce **MedReaMM**, a benchmark specifically designed to evaluate models’ ability to synthesize heterogeneous clinical evidence consisting of detailed patient histories alongside multiple medical images into accurate differential diagnoses under a complete-information paradigm. Constructed from case reports sourced from top-tier medical journals and curated clinical case databases, MedReaMM comprises 625 expert-validated cases with an average of 2.79 medical images per case and a total of 1,042 standardized diagnoses annotated with ICD-11 codes. These cases predominantly represent rare, atypical, or multi-system presentations that demand expert-level evidence integration beyond routine pattern recognition. We evaluate 23 Large Multimodal Models (LMMs) and find that most achieve diagnostic accuracy scores below 50%, underscoring a substantial gap in multimodal diagnostic synthesis capability. Further analysis reveals that medical knowledge proficiency, medical image understanding, and evidence integration are all highly correlated with diagnostic performance.</abstract>
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%0 Conference Proceedings
%T MedReaMM: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis
%A Wei, Lai
%A Chen, Yuchao
%A Cao, Zhenbiao
%A Zhang, Xiaojin
%A Wei, Zhongyu
%A Wang, Bangting
%A Chen, Wei
%A Bai, Xiang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wei-etal-2026-medreamm
%X The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus on textual reasoning or isolated visual question-answering (VQA) tasks, lacking holistic integration of clinical narratives and medical imaging, and thus failing to assess the multimodal diagnostic synthesis capability central to expert clinical judgment. To bridge this gap, we introduce **MedReaMM**, a benchmark specifically designed to evaluate models’ ability to synthesize heterogeneous clinical evidence consisting of detailed patient histories alongside multiple medical images into accurate differential diagnoses under a complete-information paradigm. Constructed from case reports sourced from top-tier medical journals and curated clinical case databases, MedReaMM comprises 625 expert-validated cases with an average of 2.79 medical images per case and a total of 1,042 standardized diagnoses annotated with ICD-11 codes. These cases predominantly represent rare, atypical, or multi-system presentations that demand expert-level evidence integration beyond routine pattern recognition. We evaluate 23 Large Multimodal Models (LMMs) and find that most achieve diagnostic accuracy scores below 50%, underscoring a substantial gap in multimodal diagnostic synthesis capability. Further analysis reveals that medical knowledge proficiency, medical image understanding, and evidence integration are all highly correlated with diagnostic performance.
%U https://aclanthology.org/2026.findings-emnlp.881/
%P 18590-18618
Markdown (Informal)
[MedReaMM: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis](https://aclanthology.org/2026.findings-emnlp.881/) (Wei et al., Findings 2026)
ACL
- Lai Wei, Yuchao Chen, Zhenbiao Cao, Xiaojin Zhang, Zhongyu Wei, Bangting Wang, Wei Chen, and Xiang Bai. 2026. MedReaMM: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 18590–18618, Budapest, Hungary. Association for Computational Linguistics.