Visual Question Answering in Robotic Surgery: A Comprehensive Review
Di Ding, Tianliang Yao, Rong Luo, Xusen Sun
Abstract
Visual Question Answering (VQA) in robotic surgery is rapidly becoming a pivotal technology in medical AI, addressing the complex challenge of interpreting multimodal surgical data to support real-time decision-making. This comprehensive review synthesizes key advancements in Surgical VQA, highlighting the integration of large language models (LLMs), multimodal fusion techniques, and visual grounding methods. By reviewing 62 key studies selected through a systematic search of major scientific databases, including IEEE Xplore, Google Scholar, SpringerLink, and PubMed, we trace the evolution of VQA systems and their application in surgical environments. Current limitations, including dataset scarcity, multimodal alignment challenges, and issues of interpretability, are critically examined. This survey aims to not only provide a structured overview of the field but also identify critical research gaps and propose future directions to enhance VQA systems for robotic surgery, with the ultimate goal of improving intraoperative performance and patient outcomes.