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Adoption Readiness of AI-Based Robotic Surgery in Head and Neck Disciplines: A Cross-Sectional Multispecialty
Sunil Kumar Gulia1, Sreenitha S Hosthor2, Satish D Mehta3
1Department of Oral and Maxillofacial Surgery, Shree Guru Gobind Singh Tricentenary University, Gurugram, IND.
Introduction:
AI-driven robotic surgery is increasingly being integrated into head and neck surgical practice. However, its clinical adoption depends on surgeons' knowledge, practical exposure, and perception. This study aimed to assess the knowledge, practice, and perception of AI-driven robotic surgery and evaluate the knowledge-practice gap among surgeons.
Materials And Methods:
A cross-sectional, questionnaire-based analytical study was conducted among 150 surgeons involved in head and neck procedures, including oral and maxillofacial surgeons (n = 42), otorhinolaryngologists (n = 38), oncologic surgeons (n = 35), and general surgeons (n = 35). A validated questionnaire assessed knowledge (six items), practice (five items), and perception (nine items). Data were analyzed statistically, with descriptive and inferential statistics. Confirmatory factor analysis and correlation analyses were performed.
Results:
High levels of knowledge were observed across specialties, with correct identification of AI-driven robotic surgery reported by 35 (83.3%) oral and maxillofacial surgeons, 30 (78.9%) otorhinolaryngologists, 32 (91.4%) oncologic surgeons, and 27 (77.1%) general surgeons. Despite this, practical exposure was limited; formal training was reported by 18 (42.9%), 14 (36.8%), 20 (57.1%), and 12 (34.3%) participants. Frequent involvement in robotic procedures was low across groups, with only eight (19.0%), six (15.8%), 12 (34.3%), and five (14.3%) surgeons reporting regular use. The perception of robotic surgery was positive, with high agreement regarding improved surgical precision and patient safety. However, high cost and lack of training were identified as major barriers. A significant knowledge-practice gap was observed across all specialties (p < 0.001). Correlation analysis demonstrated significant positive associations between knowledge and practice (r = 0.387), knowledge and perception (r = 0.341), and practice and perception (r = 0.462) (p < 0.001).
Conclusion:
Surgeons demonstrate high knowledge and a favorable perception of AI-driven robotic surgery; however, limited practical exposure highlights a significant knowledge-practice gap. Enhancing structured training programs and improving accessibility to robotic systems are essential for effective clinical integration.