ArticleCureus2026
Adoption Readiness of AI-Based Robotic Surgery in Head and Neck Disciplines: A Cross-Sectional Multispecialty Analysis.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
introductionAI-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
methodsA 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.
resultsHigh 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).
conclusionSurgeons 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.
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