Artificial Intelligence Adoption in Surgery: Cognition, Usage Patterns and Implementation Barriers of DeepSeek Among Healthcare Professionals in China’s Tertiary Hospitals

Abstract

Objective: This study aims to investigate the cognition and application status of DeepSeek among surgical medical staff in Class III Grade A hospitals and analyse its influencing factors to optimise its clinical application. Methods: -test and multivariate ordinal logistic regression analysis. Results: A total of 424 valid questionnaires were collected (96.4%). The results indicated that 67.0% of the medical staff understood the basic functions of DeepSeek, and 70.3% used DeepSeek occasionally. It was mainly used for teaching and research support (43.2%), other life services (35.6%) and patient services (29.2%). Multivariate analysis showed that medical staff working in operating rooms and neurosurgery departments, those who were occasional users, and medical staff who primarily used DeepSeek for other life services demonstrated significantly higher levels of knowledge about DeepSeek. Conclusion: Despite widespread awareness of DeepSeek's capabilities (67.0% understanding basic functions), significant implementation gaps persist, with limited clinical utilisation and predominant usage in low-risk applications. Key barriers include insufficient training (94.8% untrained), data privacy concerns (57.5%) and over-reliance fears (58.5%). These findings reveal a substantial untapped potential for AI integration in surgical practice, highlighting critical needs for targeted training interventions, enhanced data security frameworks and staged implementation protocols to bridge the awareness-utilisation gap and facilitate meaningful clinical adoption.

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