Clinicians' Decision-Making Preferences Regarding Hypothetical Nanorobotic Applications in Head and Neck Tumors: A...

Introduction  Nanorobotic technology may represent a promising advancement in precision-based surgical interventions for head and neck tumors. This study aimed to evaluate clinicians' decision-making preferences regarding hypothetical nanorobotic applications in the management of head and neck tumors using a vignette-based approach. Materials and methods  This cross-sectional, vignette-based survey was conducted among oral and maxillofacial surgeons and anesthesiologists. A validated, self-administered questionnaire comprising demographic details and six clinical scenarios was used. Each vignette offered three treatment options: conventional, robot-assisted, and nanorobotic approaches, and assessed preference, confidence, perceived safety, willingness to adopt, and primary concerns. The data were then analyzed. Descriptive statistics were computed, and associations were evaluated using the chi-square test. Ordinal variables were analyzed using the Kruskal-Wallis test with Dunn's post hoc correction. Results  A total of 120 clinicians participated in the study. Nanorobotic approaches were significantly preferred in lymph node metastasis, deep-seated tumors, and medically compromised patients compared to conventional and robot-assisted approaches (p < 0.001), whereas conventional surgery was favored in facial nerve tumors (p = 0.012) and microvascular reconstruction (p = 0.008). No significant difference in treatment preference among conventional, robot-assisted, and nanorobotic approaches was observed in recurrent tumor scenarios (p = 0.843). The mean confidence and perceived safety scores for the nanorobotic approach were highest in deep-seated tumors (3.9 ± 0.8 and 3.8 ± 0.7, respectively) and lowest in reconstructive scenarios (3.1 ± 1.0 and 2.9 ± 1.1, respectively). Across all vignette responses, 49.7% indicated willingness to adopt nanorobotic interventions. Prior robotic surgery experience showed a significant association with preference for nanorobotics (p = 0.039). Cost, lack of evidence, training, and safety-related concerns were commonly reported barriers to nanorobotic adoption, and nanotechnology familiarity significantly influenced confidence and perceived safety (p < 0.001). Conclusion  Clinician acceptance of nanorobotic interventions is scenario-dependent and influenced by prior technological exposure, with safety, control, cost, lack of evidence, and training requirements remaining important concerns and barriers to adoption. While nanorobotic approaches show promise in precision-driven and minimally invasive scenarios, their integration into routine clinical practice will require robust evidence, structured training, and technological refinement. Clinically, these findings suggest that early adoption may be most feasible in selected cases requiring high precision and limited access, and that targeted clinician education and simulation-based training could facilitate smoother translation into practice.