SurgMLLMBench: A Multimodal Large Language Model Benchmark Dataset for Surgical Scene Understanding

• URL: http://arxiv.org/abs/2511.21339v1
• Date: Wed, 26 Nov 2025 12:44:51 GMT
• Title: SurgMLLMBench: A Multimodal Large Language Model Benchmark Dataset for Surgical Scene Understanding
• Authors: Tae-Min Choi, Tae Kyeong Jeong, Garam Kim, Jaemin Lee, Yeongyoon Koh, In Cheul Choi, Jae-Ho Chung, Jong Woong Park, Juyoun Park,
• Abstract summary: We present SurgMLLMBench, a unified benchmark for developing and evaluating interactive multimodal large language models.<n>It integrates pixel-level instrument segmentation masks and structured VQA annotations across laparoscopic, robot-assisted, and micro-surgical domains.<n>It achieves consistent performance across domains and generalizes effectively to unseen datasets.
• Score: 8.20483591990742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in multimodal large language models (LLMs) have highlighted their potential for medical and surgical applications. However, existing surgical datasets predominantly adopt a Visual Question Answering (VQA) format with heterogeneous taxonomies and lack support for pixel-level segmentation, limiting consistent evaluation and applicability. We present SurgMLLMBench, a unified multimodal benchmark explicitly designed for developing and evaluating interactive multimodal LLMs for surgical scene understanding, including the newly collected Micro-surgical Artificial Vascular anastomosIS (MAVIS) dataset. It integrates pixel-level instrument segmentation masks and structured VQA annotations across laparoscopic, robot-assisted, and micro-surgical domains under a unified taxonomy, enabling comprehensive evaluation beyond traditional VQA tasks and richer visual-conversational interactions. Extensive baseline experiments show that a single model trained on SurgMLLMBench achieves consistent performance across domains and generalizes effectively to unseen datasets. SurgMLLMBench will be publicly released as a robust resource to advance multimodal surgical AI research, supporting reproducible evaluation and development of interactive surgical reasoning models.

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