Artificial Intelligence-Empowered "Walking Hospitals": A Narrative Review of Innovative Models and Ecosystem...

Remote mountainous areas face severe challenges regarding medical resource scarcity and delayed emergency treatment. While the "Walking Hospital" model has physically descended medical hardware to the village level, the efficacy of these interventions is bottlenecked by the limited diagnostic capabilities of grassroots doctors.

This narrative review synthesizes literature identified through searches in PubMed, IEEE Xplore, and Web of Science, focusing on articles published between January 2017 and March 2026. Keywords included "mobile health", "artificial intelligence", "telemedicine", "rural health", "drone logistics", and "edge computing". We prioritized peer-reviewed articles, clinical trials, and policy analyses relevant to resource-limited settings. Due to the heterogeneity of study designs and the emerging nature of the topic, a formal meta-analysis was not conducted; instead, a qualitative synthesis of technological models and operational frameworks is presented.

The review finds that the deep integration of edge computing, natural language processing (NLP), and computer vision (CV) empowers village doctors with specialist-level diagnostic capabilities offline. Technically, lightweight AI models enable real-time ECG interpretation and ultrasound guidance in network dead zones. Operationally, a closed-loop ecosystem integrating Low-Earth-Orbit (LEO) satellite communications, medical drone logistics, and county-level medical consortia is identified as a sustainable framework. Global case studies from Rwanda, India, and Australia validate the feasibility of AI-optimized aerial logistics and edge-based diagnostics in resource-limited settings. However, critical barriers remain, including algorithmic generalization deficits (domain shift) and ambiguous liability frameworks.

AI-empowered "Walking Hospitals" represent a paradigm shift from hardware distribution to capability enhancement. Future research must prioritize resolving domain shift through techniques like Federated Learning, establishing sustainable reimbursement models, and developing community-level data governance frameworks to transition these innovations from pilot projects to scalable global solutions.