Empowering Surgical Training through Artificial Intelligence: A Cross-Sectional Study on Residents’ Acceptance and Perceived Usefulness of AI-Based Simulation

Background: Artificial intelligence (AI)-based simulation is an emerging innovation in surgical education, offering a safe, interactive, and data-driven environment for skill development. Traditional apprenticeship models are increasingly limited by reduced operative exposure, patient safety concerns, and variability in clinical training. Understanding residents’ acceptance of AI technologies is essential for effective integration into surgical education. Aim: This study aimed to assess surgical residents’ acceptance and perceived usefulness of AI-based simulation in surgical training at Saidu Teaching Hospital, Swat. Methodology: An analytical cross-sectional study was conducted among 80 surgical residents from various specialties using non-probability convenience sampling. Data were collected through a structured, self-administered questionnaire based on the Technology Acceptance Model, including demographic variables and Likert-scale items assessing perceived usefulness and acceptance. Data were analyzed using statistical package for the social sciences (SPSS) version 27. Descriptive statistics, Pearson correlation, independent t-tests, and one-way ANOVA were applied, with p ≤ 0.05 considered statistically significant. Results: Residents reported high perceived usefulness (mean 3.87 ± 0.58) and acceptance (mean 3.74 ± 0.62) of AI-based simulation. A strong positive correlation was found between perceived usefulness and acceptance (r = 0.68, p < 0.001). No significant gender differences were observed, while acceptance varied significantly across training years (p = 0.041). Conclusion: AI-based simulation is well accepted among surgical residents. Early integration, faculty training, and blended learning approaches are recommended to enhance surgical skill acquisition.