Artificial Intelligence–Driven Personalized Learning Improves Operating Room Instrument Training: A Prospective Observational Study

Traditional operating room instrument training often relies on one-size-fits-all teaching, produces poor long-term skill retention, and contributes to preventable surgical errors. The present study hypothesized that an artificial intelligence (AI) powered personalized learning system could improve technical competency, patient safety, and training efficiency by tailoring practice to individual learner profiles. An AI-powered personalized learning system (APLS) was developed that combines data-driven learner phenotyping, deep-learning-based instrument recognition, ensemble prediction of competency trajectories, and reinforcement learning to deliver adaptive multimodal feedback during simulation-based training. In a prospective observational study with cluster-based departmental allocation (introducing quasi-experimental elements) at a tertiary hospital, 107 multidisciplinary operating room staff completed either the AI-driven curriculum or standard instructor-led training. The primary outcome was 12-month retention of instrument-handling competency, measured by the perioperative instrument proficiency scale (PIPS); secondary outcomes included operating room safety incidents, training time, and cost per competent professional. Compared with traditional training, the personalized system was associated with substantially higher 12-month competency scores (APLS 84.1 ± 9.2 vs. Control 58.9 ± 18.7, p < 0.001), fewer safety-related events in clinical practice, and nearly half the training time while reducing overall training costs by 38.3%. The AI ensemble also outperformed its individual machine-learning components when predicting learner performance and selecting feedback strategies. These findings suggest that integrating unsupervised phenotype discovery, supervised prediction, and reinforcement learning into a unified platform can meaningfully enhance operating room instrument training and may offer a scalable framework for data-driven workforce development in perioperative care.