Real-World Impact and Educational Effectiveness of an AI-Powered Medical History-Taking System: Retrospective Propensity Score-Matched Cohort Study

Background Medical history-taking is a core clinical skill; yet, traditional teaching methods face challenges. We developed an artificial intelligence–powered medical history-taking training and evaluation system (AMTES) and established its technical feasibility as an extracurricular resource. Evidence on whether such tools improve learning outcomes when voluntarily embedded in routine curricula remains limited. Objective This study aimed to evaluate the real-world educational effectiveness of AMTES as an opt-in extracurricular tool and examine whether learning gains vary by practice patterns and baseline academic ability. Methods We conducted a retrospective cohort study of the 2024-2025 Diagnostics course cohort (N=478) at Shantou University Medical College, China, using total population sampling. Students were categorized as AMTES users (n=205, 42.9%; ≥1 sessions) and nonusers (n=273, 57.1%) based on their voluntary extracurricular adoption of the system during the month preceding a high-stakes final practical skills examination. To address selection bias, we performed 1:1 propensity score matching via logistic regression using age, sex, and 3 previous academic scores as covariates. The average treatment effect on the treated for final examination score (0-70) was estimated with paired t tests, and robustness to unobserved confounding was assessed via Rosenbaum sensitivity analysis. Among matched users, practice patterns were identified using K-means clustering on log-derived features, with cluster differences compared using Mann-Whitney U tests. Subsequently, we explored aptitude-treatment interaction by testing the interaction between practice intensity and baseline ability using linear and logistic regression models. Results Propensity score matching yielded 157 matched pairs (n=314) with excellent covariate balance (|standardized mean difference|