Visual Question Answering in Robotic Surgery: A Comprehensive Review
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, highlight...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10820517/ |
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| author | Di Ding Tianliang Yao Rong Luo Xusen Sun |
| author_facet | Di Ding Tianliang Yao Rong Luo Xusen Sun |
| author_sort | Di Ding |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-465ca1a27e274e1b9e2153d5abd579de |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-465ca1a27e274e1b9e2153d5abd579de2025-02-05T00:00:50ZengIEEEIEEE Access2169-35362025-01-01139473948410.1109/ACCESS.2024.352514510820517Visual Question Answering in Robotic Surgery: A Comprehensive ReviewDi Ding0https://orcid.org/0009-0005-2435-4019Tianliang Yao1https://orcid.org/0009-0000-7063-3880Rong Luo2https://orcid.org/0009-0000-9892-1315Xusen Sun3https://orcid.org/0009-0006-8607-1265Department of Cardiology, Qingpu Branch of Zhongshan Hospital, Fudan University, Shanghai, ChinaDepartment of Control Science and Engineering, College of Electronic and Information Engineering, Tongji University, Shanghai, ChinaDepartment of Cardiology, Qingpu Branch of Zhongshan Hospital, Fudan University, Shanghai, ChinaDepartment of Cardiology, Qingpu Branch of Zhongshan Hospital, Fudan University, Shanghai, ChinaVisual 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.https://ieeexplore.ieee.org/document/10820517/Surgical visual question answeringmultimodal learningrobotic surgeryvisual groundingmedical AI |
| spellingShingle | Di Ding Tianliang Yao Rong Luo Xusen Sun Visual Question Answering in Robotic Surgery: A Comprehensive Review IEEE Access Surgical visual question answering multimodal learning robotic surgery visual grounding medical AI |
| title | Visual Question Answering in Robotic Surgery: A Comprehensive Review |
| title_full | Visual Question Answering in Robotic Surgery: A Comprehensive Review |
| title_fullStr | Visual Question Answering in Robotic Surgery: A Comprehensive Review |
| title_full_unstemmed | Visual Question Answering in Robotic Surgery: A Comprehensive Review |
| title_short | Visual Question Answering in Robotic Surgery: A Comprehensive Review |
| title_sort | visual question answering in robotic surgery a comprehensive review |
| topic | Surgical visual question answering multimodal learning robotic surgery visual grounding medical AI |
| url | https://ieeexplore.ieee.org/document/10820517/ |
| work_keys_str_mv | AT diding visualquestionansweringinroboticsurgeryacomprehensivereview AT tianliangyao visualquestionansweringinroboticsurgeryacomprehensivereview AT rongluo visualquestionansweringinroboticsurgeryacomprehensivereview AT xusensun visualquestionansweringinroboticsurgeryacomprehensivereview |