Facing of mounting resource constraints and rising demands for personalization in medical education, regional anatomy teaching urgently requires transformation. In this paper, we focus on the regional anatomy of the thoracic wall, in order to explore a novel AI-driven teaching paradigm. Anchored in the core principle of “virtual-real integration with cadaveric dissection as the cornerstone,” the paradigm redefines educational objective  and constructs an intelligent, closed-loop teaching model integrating students, computers, and instructors. Leveraging the robust support of digital intelligence (e.g. , DeepSeek), this paradigm incorporates interactive method including group collaboration, branching instruction, and gamified assessments. It achieves a comprehensive intelligent transformation of the entire teaching process—from goal setting and plan customization to activity implementation, task completion, outcome exchange, multidimensional evaluation, and reflective iteration. This new paradigm centers on medical students and leverages digital intelligence to activate deep personalized learning potential. It seamlessly integrates fundamental anatomical knowledge with clinical scenarios (e.g. , key anatomy in breast cancer surgery, flap design in breast reconstruction), and significantly enhances clinical decision-making abilities, scientific research and innovative thinking, as well as medical humanistic literacy, paving a new path for intelligent medical education.