AI可提升偏远地区医疗可及性与公平性，助力解决人力与资源短缺问题。
Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation
- 系统综述109项研究，发现AI在远程诊疗、智能诊断中效果显著
- 多模态大模型与大语言模型能整合影像、病历等数据，提升决策效率
- 适合关注智慧医疗、数字健康公平性的研究人员与政策制定者
农村医疗长期面临基础设施不足、医护人力短缺及社会经济差距导致的服务可及性问题。本研究系统回顾了2019至2024年间发表于PubMed、Embase、Web of Science、IEEE Xplore和Scopus的109篇文献，采用PRISMA指南与Covidence软件进行筛选。通过主题分析识别出人工智能在农村医疗中的关键应用模式。结果表明，预测分析、远程医疗平台与自动化诊断工具在提升服务可及性、质量和效率方面具有显著潜力。其中，多模态基础模型（MFMs）与大语言模型（LLMs）表现尤为突出：前者融合影像、临床记录与生物信号数据支持综合决策，后者实现临床文书生成、患者分诊、翻译与虚拟助手功能。这些技术可增强人力能力、缩短诊断延迟、推动专业资源普及。然而仍存在基础设施薄弱、数据质量差与伦理风险等障碍。需跨学科协作、加强数字基建投入并建立监管框架。本文提出可操作建议，并指明未来研究方向，以实现农村医疗AI的公平与可持续融合。
原文摘要 · Abstract (English)
Rural healthcare faces persistent challenges, including inadequate infrastructure, workforce shortages, and socioeconomic disparities that hinder access to essential services. This study investigates the transformative potential of artificial intelligence (AI) in addressing these issues in underserved rural areas. We systematically reviewed 109 studies published between 2019 and 2024 from PubMed, Embase, Web of Science, IEEE Xplore, and Scopus. Articles were screened using PRISMA guidelines and Covidence software. A thematic analysis was conducted to identify key patterns and insights regarding AI implementation in rural healthcare delivery. The findings reveal significant promise for AI applications, such as predictive analytics, telemedicine platforms, and automated diagnostic tools, in improving healthcare accessibility, quality, and efficiency. Among these, advanced AI systems, including Multimodal Foundation Models (MFMs) and Large Language Models (LLMs), offer particularly transformative potential. MFMs integrate diverse data sources, such as imaging, clinical records, and bio signals, to support comprehensive decision-making, while LLMs facilitate clinical documentation, patient triage, translation, and virtual assistance. Together, these technologies can revolutionize rural healthcare by augmenting human capacity, reducing diagnostic delays, and democratizing access to expertise. However, barriers remain, including infrastructural limitations, data quality concerns, and ethical considerations. Addressing these challenges requires interdisciplinary collaboration, investment in digital infrastructure, and the development of regulatory frameworks. This review offers actionable recommendations and highlights areas for future research to ensure equitable and sustainable integration of AI in rural healthcare systems.
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