Design concept for a neurology metaverse teaching system based on the Fudan Zhongshan "huisheng intelligent education" platform

Specialty education in neurology faces structural bottlenecks including insufficient training in low-frequency, high-risk clinical scenarios, unequal opportunities for performing key procedures, difficulty in replicating ethically sensitive communication situations, and a weak cross-stage evaluation system. Based on the “Huisheng Intelligent Education” metaverse platform of Zhongshan Hospital, Fudan University, this paper proposes a conceptual framework for building a neurology-specific metaverse platform targeting full-cycle medical education, training, and management. Using extended reality (XR) and artificial intelligence (AI) as the technological foundation, and focusing on core teaching scenarios such as neurological examination, emergency decision-making in stroke, status epilepticus management, long-term care of Parkinson‘s disease, and breaking bad news, the platform constructs four major modules: a neurology knowledge ecosystem, immersive virtual teaching scenarios, a specialized teaching intelligence system, and a full-cycle teaching management hub. The platform supports cross-campus synchronous teaching and process tracking, aiming to establish a new paradigm of specialty education characterized by “scenario-based training — formative evaluation — personalized guidance — governance closed loop”. Taking the “acute ischemic stroke thrombolysis decision-making metaverse teaching unit” as an example, this paper demonstrates the feasible approach of the platform in scenario design, task chain organization, and technical implementation. This paper provides a replicable engineering framework and design reference for the systematic construction of metaverse teaching in neurology, with future effectiveness validation to be conducted based on actual operational data.

Key Words: neurology; metaverse in medicine; full-cycle medical education; immersive training; thrombolysis decision-making; competency evaluation