@inproceedings{liu-etal-2026-benchmarking-egocentric,
title = "Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models",
author = "Liu, Shaonan and
Yu, Guo and
Luo, Xiaoling and
Zheng, Shiyi and
Liu, Jie and
Chen, Wenting and
Shen, Linlin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1228/",
pages = "26682--26697",
ISBN = "979-8-89176-390-6",
abstract = "Medical Multimodal Large Language Models (Med-MLLMs) require egocentric clinical intent understanding for real-world deployment, yet existing benchmarks fail to evaluate this critical capability. We introduce MedGaze-Bench, the first benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation, and diagnostic interpretation. Our benchmark addresses three fundamental challenges: visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical workflows, and implicit adherence to safety protocols. We propose a Three-Dimensional Clinical Intent Framework evaluating: (1) Spatial Intent{---}discriminating precise targets amid visual noise, (2) Temporal Intent{---}inferring causal rationale through retrospective and prospective reasoning, and (3) Standard Intent{---}verifying protocol compliance through safety checks. Beyond accuracy metrics, we introduce Trap QA mechanisms to stress-test clinical reliability by penalizing hallucinations and cognitive sycophancy. Experiments reveal current MLLMs struggle with egocentric intent due to over-reliance on global features, leading to fabricated observations and uncritical acceptance of invalid instructions."
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<abstract>Medical Multimodal Large Language Models (Med-MLLMs) require egocentric clinical intent understanding for real-world deployment, yet existing benchmarks fail to evaluate this critical capability. We introduce MedGaze-Bench, the first benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation, and diagnostic interpretation. Our benchmark addresses three fundamental challenges: visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical workflows, and implicit adherence to safety protocols. We propose a Three-Dimensional Clinical Intent Framework evaluating: (1) Spatial Intent—discriminating precise targets amid visual noise, (2) Temporal Intent—inferring causal rationale through retrospective and prospective reasoning, and (3) Standard Intent—verifying protocol compliance through safety checks. Beyond accuracy metrics, we introduce Trap QA mechanisms to stress-test clinical reliability by penalizing hallucinations and cognitive sycophancy. Experiments reveal current MLLMs struggle with egocentric intent due to over-reliance on global features, leading to fabricated observations and uncritical acceptance of invalid instructions.</abstract>
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%0 Conference Proceedings
%T Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models
%A Liu, Shaonan
%A Yu, Guo
%A Luo, Xiaoling
%A Zheng, Shiyi
%A Liu, Jie
%A Chen, Wenting
%A Shen, Linlin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liu-etal-2026-benchmarking-egocentric
%X Medical Multimodal Large Language Models (Med-MLLMs) require egocentric clinical intent understanding for real-world deployment, yet existing benchmarks fail to evaluate this critical capability. We introduce MedGaze-Bench, the first benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation, and diagnostic interpretation. Our benchmark addresses three fundamental challenges: visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical workflows, and implicit adherence to safety protocols. We propose a Three-Dimensional Clinical Intent Framework evaluating: (1) Spatial Intent—discriminating precise targets amid visual noise, (2) Temporal Intent—inferring causal rationale through retrospective and prospective reasoning, and (3) Standard Intent—verifying protocol compliance through safety checks. Beyond accuracy metrics, we introduce Trap QA mechanisms to stress-test clinical reliability by penalizing hallucinations and cognitive sycophancy. Experiments reveal current MLLMs struggle with egocentric intent due to over-reliance on global features, leading to fabricated observations and uncritical acceptance of invalid instructions.
%U https://aclanthology.org/2026.acl-long.1228/
%P 26682-26697
Markdown (Informal)
[Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models](https://aclanthology.org/2026.acl-long.1228/) (Liu et al., ACL 2026)
ACL
- Shaonan Liu, Guo Yu, Xiaoling Luo, Shiyi Zheng, Jie Liu, Wenting Chen, and Linlin Shen. 2026. Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 26682–26697, San Diego, California, United States. Association for Computational Linguistics.