AI-enhanced case-based learning for clinical reasoning in pathophysiology: A sequential cohort study
Abstract
Abstract Background Pathophysiology bridges basic sciences and clinical medicine. However, fragmented knowledge delivery, static case-based learning (CBL), and insufficient training in clinical reasoning remain common challenges. This study developed and evaluated an artificial intelligence (AI)-enhanced CBL model to address these issues. Methods A sequential cohort study was conducted at Nanchang University, China, involving students in an innovative medical program. We implemented a framework across the pathophysiology curriculum centered on three components: curriculum restructuring, clinical problem analysis, and innovative research design. An AI-powered platform provided pre-class personalized resources, in-class analytics to facilitate discussion, and post-class multi-dimensional assessment with automated feedback. Learning outcomes were assessed through knowledge tests, platform analytics, and student surveys, comparing the intervention cohort (Class of 2023, n = 49) with a traditional cohort (Class of 2022, n = 47). The two cohorts had comparable baseline academic profiles. Results The intervention cohort scored significantly higher on the final examination (88.59 vs. 83.14, p 0.05). Platform analytics showed engagement patterns aligned with clinical reasoning tasks. Survey data (89.8% response rate; 44/49 students) indicated that 84.1% of students were satisfied with the reformed model, and 75.7% reported improved understanding of pathomechanisms and clinical…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 49 participants or records.
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