Evidence map›Paper›PMID 42701665›Full record

ReviewInternational journal of general medicine2026

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

Wei Lu, Gang Feng, Man Tan, Pengjuan Weng, Wenxiang Zhu

Abstract readReview
In one paragraph

Review in International journal of general medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Wei LuThe Geriatric Department of People's Hospital of Wushan County, Chongqing, Mainland, People's Republic of China.
Gang FengThe Geriatric Department of People's Hospital of Wushan County, Chongqing, Mainland, People's Republic of China.
Man TanThe Geriatric Department of People's Hospital of Wushan County, Chongqing, Mainland, People's Republic of China.
Pengjuan WengThe Geriatric Department of People's Hospital of Wushan County, Chongqing, Mainland, People's Republic of China.
Wenxiang ZhuThe Geriatric Department of People's Hospital of Wushan County, Chongqing, Mainland, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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. Methods: 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. Results: 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. Conclusion: 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.

Indexed as

artificial intelligencedigital divideedge computingfederated learningglobal healthmedical dronesremote mountainous areastelemedicinewalking hospitals

Identifiers

PMID42701665
PMCPMC13546061

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.