Breaking Through the Challenges in Medical Mathematics Education: An Innovative Practice Based on the Intelligent Case Teaching Model

Background and aim In the evolution from empirical medicine to evidence - based medicine and then to precision medicine, mathematical modeling and computational capabilities have emerged as critical bottlenecks constraining the realization of precision healthcare. There is an urgent need to establish a novel teaching model grounded in advanced mathematics and oriented toward clinical problems.This paper addresses three core challenges in medical mathematics education: prominent disciplinary barriers, scarce teaching resources, and ambiguous evaluation mechanisms. It does so by proposing a new paradigm for cultivating mathematical literacy tailored to precision medicine. Method This study integrated 239 authentic medical cases from clinical medicine, pharmacy, public health, and health management into higher mathematics classroom instruction. It established an integrated curriculum system centered on a four - dimensional closed - loop teaching model—“Principle - Case - Data - Practice” (PCDP), effectively bridging advanced mathematics with precision medicine. To address three major challenges in medical mathematics education—disciplinary fragmentation, resource scarcity, and ambiguous assessment—the study introduced an intelligent teaching ecosystem featuring “Three Teaching Methods - Four Learning Methods - Five - Dimensional Evaluation.” It proposed a teaching reform framework centered on “Medicine - Driven—Implicit Cases—Intelligent Empowerment.” Powered by an AI - driven question bank covering 152 tiered knowledge points, the system implements a multidimensional evaluation mechanism that emphasizes both process and outcomes. Results The implementation of the reform yielded significant outcomes. Student academic performance improved markedly, with the average score rising from 71.4 to 86.7, and the proportion of high-scoring students (≥ 90 points) increasing substantially from 9.4% to 42.7%. The course received over 3,100 evaluations, demonstrating widespread student approval. More importantly, students' mathematical modeling thinking and data analysis skills were significantly enhanced. In subsequent data analysis modules, students in the case - based teaching group significantly outperformed the control group.This model has been implemented across three majors and two affiliated hospitals within the university, reaching over 1,200 students annually, which confirms its replicability and scalability potential. Conclusion This model nurtures a scalable and sustainable teaching ecosystem, offering a practice - validated path for cultivating interdisciplinary medical professionals in the era of precision medicine.