@inproceedings{chen-etal-2026-medsyn,
title = "{MEDSYN}: Benchmarking Multi-{E}vi{D}ence {SYN}thesis in Complex Clinical Cases for Multimodal Large Language Models",
author = "Chen, Boqi and
Liu, Xudong and
Peng, Jiachuan and
Frey-Marti, Marianne and
Lam, Kyle and
Zheng, Bang and
Li, Lin and
Qiu, Jianing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1183/",
pages = "23631--23655",
ISBN = "979-8-89176-395-1",
abstract = "Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity. We introduce MEDSYN, a multilingual, multimodal benchmark of highly complex clinical cases with up to 7 distinct visual clinical evidence (CE) types per case. Mirroring clinical workflow, we evaluate 18 MLLMs on differential diagnosis (DDx) generation and final diagnosis (FDx) selection. While frontier models often match or even outperform human experts on DDx generation, all MLLMs exhibit a much larger DDx{--}FDx performance gap compared to expert clinicians, indicating a failure mode in synthesis of heterogeneous CE types. Ablations attribute this failure to (i) overreliance on less discriminative textual CE (\textit{e.g.}, medical history) and (ii) a cross-modal CE utilization gap. We introduce \textit{Evidence Sensitivity} to quantify the latter and show that a smaller gap correlates with higher diagnostic accuracy. Finally, we demonstrate how it can be used to guide interventions to improve model performance. https://github.com/jianing-lab/MEDSYN."
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<abstract>Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity. We introduce MEDSYN, a multilingual, multimodal benchmark of highly complex clinical cases with up to 7 distinct visual clinical evidence (CE) types per case. Mirroring clinical workflow, we evaluate 18 MLLMs on differential diagnosis (DDx) generation and final diagnosis (FDx) selection. While frontier models often match or even outperform human experts on DDx generation, all MLLMs exhibit a much larger DDx–FDx performance gap compared to expert clinicians, indicating a failure mode in synthesis of heterogeneous CE types. Ablations attribute this failure to (i) overreliance on less discriminative textual CE (e.g., medical history) and (ii) a cross-modal CE utilization gap. We introduce Evidence Sensitivity to quantify the latter and show that a smaller gap correlates with higher diagnostic accuracy. Finally, we demonstrate how it can be used to guide interventions to improve model performance. https://github.com/jianing-lab/MEDSYN.</abstract>
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%0 Conference Proceedings
%T MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models
%A Chen, Boqi
%A Liu, Xudong
%A Peng, Jiachuan
%A Frey-Marti, Marianne
%A Lam, Kyle
%A Zheng, Bang
%A Li, Lin
%A Qiu, Jianing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chen-etal-2026-medsyn
%X Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity. We introduce MEDSYN, a multilingual, multimodal benchmark of highly complex clinical cases with up to 7 distinct visual clinical evidence (CE) types per case. Mirroring clinical workflow, we evaluate 18 MLLMs on differential diagnosis (DDx) generation and final diagnosis (FDx) selection. While frontier models often match or even outperform human experts on DDx generation, all MLLMs exhibit a much larger DDx–FDx performance gap compared to expert clinicians, indicating a failure mode in synthesis of heterogeneous CE types. Ablations attribute this failure to (i) overreliance on less discriminative textual CE (e.g., medical history) and (ii) a cross-modal CE utilization gap. We introduce Evidence Sensitivity to quantify the latter and show that a smaller gap correlates with higher diagnostic accuracy. Finally, we demonstrate how it can be used to guide interventions to improve model performance. https://github.com/jianing-lab/MEDSYN.
%U https://aclanthology.org/2026.findings-acl.1183/
%P 23631-23655
Markdown (Informal)
[MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models](https://aclanthology.org/2026.findings-acl.1183/) (Chen et al., Findings 2026)
ACL
- Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Kyle Lam, Bang Zheng, Lin Li, and Jianing Qiu. 2026. MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 23631–23655, San Diego, California, United States. Association for Computational Linguistics.