Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models

• URL: http://arxiv.org/abs/2601.06750v1
• Date: Sun, 11 Jan 2026 02:20:40 GMT
• Title: Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models
• Authors: Shaonan Liu, Guo Yu, Xiaoling Luo, Shiyi Zheng, Wenting Chen, Jie Liu, Linlin Shen,
• Abstract summary: 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.<n>Our benchmark addresses three fundamental challenges: visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical, and implicit adherence to safety protocols.
• Score: 48.95516224614331
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. To address these challenges, 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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