Objective To 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. Methods Chinese 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. Results Existing 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. Conclusion The 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.

Citation： ZHENG Dan, LUO Ting. Case-based teaching method based on shared multimodal fusion imaging database and artificial intelligence-assisted diagnosis: its practical value in ultrasound physician training in primary care settings. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2026, 33(8): 1047-1054. doi: 10.7507/1007-9424.202604036 Copy

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