Objective To evaluate the teaching effect of case-based learning (CBL) combined with team-based learning (TBL) pedagogy in laboratory diagnostics education. Methods The data of the undergraduate clinical-medical students who completed the laboratory diagnostics course between September 2021 and July 2023 at West China School of Medicine of Sichuan University were retrospectively collected. Among them, the undergraduates of grade 2020 adopted the CBL+TBL combined pedagogy, while the undergraduates of grade 2019 assumed the traditional lecture-based learning (LBL) teaching method. Based on the cluster sampling method with the whole grade as the sampling unit, comparative effectiveness was assessed via examination scores and questionnaire feedback. Results A total of 303 students received the CBL+TBL teaching method (CBL+TBL group), while 289 students received the LBL teaching method (LBL group). Compared with the LBL group, the CBL+TBL group had higher scores on the clinical case discussion (90.28±4.05 vs. 88.39±5.19, P<0.001) while lower scores on the theoretical quiz (83.89±12.55 vs. 88.77±10.46, P<0.001). The overall performance of the CBL+TBL group was worse than the LBL group (61.28±3.83 vs. 61.98±3.98, P=0.028). A total of 161 valid copies of questionnaire were collected from the CBL+TBL group. Among them, more than 70% of the students believed that CBL+TBL pedagogy could improve learning motivation, independent learning ability, and team communication and assistance ability. Compared with the LBL teaching mode, 74.5% of the students preferred the CBL+TBL pedagogical approach. Conclusion The CBL+TBL pedagogy can help to improve autonomic learning, teamwork, and problem-solving ability, and a combination with LBL teaching may be utilized to enhance theoretical scores.
ObjectiveTo address the shortage of training resources for breast sonographers in primary care settings, examine the theoretical feasibility of integrating multimodal imaging databases, artificial intelligence (AI)-assisted diagnosis, and case-based learning (CBL) into breast cancer screening training, develop an integrated teaching model, and evaluate its practical value. MethodsChinese and English articles on CBL, multimodal breast imaging, and AI-assisted diagnosis in the fields of medical education and breast cancer screening were searched; the independent application status and bottlenecks of these three technologies in breast ultrasound screening training in primary care settings were systematically reviewed, and the theoretical logic for their integration was elucidated. ResultsExisting evidence shows that multimodal breast imaging and AI are widely used in breast disease diagnosis and research, and CBL with multimodal breast imaging significantly improves breast ultrasound training outcomes. AI-powered training incorporating multimodal breast imaging has been shown to outperform conventional offline didactic instruction in both cost and efficiency. Yet existing studies have largely investigated teaching applications of the three technologies individually, rather than in an integrated manner. Grounded in robust existing evidence, this review systematically delineate the intrinsic theoretical underpinnings linking three core components: a shared multimodal breast imaging database, dual-function AI tools for both diagnostic assistance and pedagogical feedback, and the CBL pedagogy. We further propose an integrated CBL-based training model that synthesizes a shared multimodal breast imaging database with a dual-function AI platform supporting both diagnostic and educational workflows (hereafter referred to as the “integrated CBL training model”). This model is expected to enable standardized teaching case provision and AI-driven real-time feedback across the full diagnostic workflow. Theoretically, it overcomes the spatiotemporal constraints inherent in conventional offline training programs, provides a streamlined, accessible pathway for primary care ultrasound physicians to undergo standardized competency training, and thereby strengthens breast cancer screening capacity in the primary care level. ConclusionThe integrated CBL training model is expected to reduce the time to competency for primary care ultrasound physicians and, in turn, strengthen breast cancer screening capacity across primary care settings.