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arXiv · 2511.21339

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

Tae-Min Choi·Tae Kyeong Jeong·Garam Kim·Jaemin Lee·Yeongyoon Koh·In Cheul Choi·Jae-Ho Chung·Jong Woong Park·Juyoun Park
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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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Tae-Min Choi, Tae Kyeong Jeong, Garam Kim, Jaemin Lee, Yeongyoon Koh, In Cheul Choi, Jae-Ho Chung, Jong Woong Park, Juyoun Park. 2025-11-26. SurgMLLMBench: A Multimodal Large Language Model Benchmark Dataset for Surgical Scene Understanding. https://arxiv.org/abs/2511.21339

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