Determinants of Artificial Intelligence Adoption in Aesthetic Practice: A Cross-Sectional Study

The integration of artificial intelligence (AI) into plastic and aesthetic surgery is advancing rapidly, with applications in preoperative image analysis, outcome simulation, and complication prediction. However, the adoption of AI-based systems in clinical practice remains limited, and the psychological and structural determinants of adoption among plastic surgery physicians are not fully understood. This study examines the cognitive, emotional, and structural factors influencing AI adoption intentions within an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework.

A quantitative cross-sectional survey was conducted among 102 physicians, including specialists, senior residents, and certified cosmetic medicine practitioners. The extended UTAUT model incorporates performance expectancy, effort expectancy, AI anxiety, and perceived barriers as determinants of behavioural intention. Data were analysed using exploratory factor analysis, Pearson's correlation, and multiple linear regression.

Performance expectancy (β = 0.431, p < 0.001) and effort expectancy (β = 0.287, p < 0.001) significantly and positively predicted AI adoption intention, together explaining 52% of the variance (R² = 0.52). Conversely, AI anxiety (β = - 0.251, p < 0.001) and perceived barriers (β = - 0.189, p = 0.004) had significant negative effects. Demographic variables, including age and professional experience, showed no statistically significant influence on adoption intention.

In plastic and aesthetic surgery, perceived clinical utility and ease of use are the primary drivers of AI adoption, whereas emotional concerns and structural barriers act as significant impediments. Targeted educational programmes, transparent AI systems, and ethically grounded regulatory frameworks are essential for fostering sustainable AI integration into aesthetic surgical practice.

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