Data Sheet 1_Exploring plausible futures for artificial intelligence in rural healthcare: insights from participatory foresight methods.pdf

BackgroundArtificial intelligence (AI) has the potential to transform rural healthcare delivery through automated monitoring, personalised care, and virtual support. Yet the future pathways for AI in rural contexts remain underexplored. Most AI applications are developed in urban-centric environments with limited consideration for infrastructure constraints, workforce realities, and sociocultural dynamics that shape rural healthcare delivery.
MethodsThis study examined stakeholder perspectives on the future role of AI in rural healthcare, identifying key priorities, facilitators, and barriers to adoption. Using a participatory research approach incorporating horizon scanning and foresight methods, data were collected during a structured workshop at the South Australian Rural Health Research and Education Conference. Forty participants, including general practitioners, clinicians, medical students, researchers, and healthcare administrators, engaged in four sequential activities: historical events mapping, future event possibilities, experiential future scenarios, and priority setting using the MoSCoW framework. Written responses were systematically transcribed and analysed using reflexive thematic analysis.
ResultsFour prominent themes emerged capturing stakeholder priorities and the guardrails they considered essential for future technological integration. These themes related to opportunities from AI and technology deployment for rural and remote equity, people at the centre of care, ethical challenges, and funding and systems issues. Participants acknowledged AI's potential to reduce geographical barriers and improve access to healthcare services, while also raising concerns about data privacy, governance, cultural appropriateness, and the risk of technology exacerbating existing health disparities. Across activities, participants expressed a strong preference for AI that supports rather than replaces human clinicians, and emphasised the importance of maintaining person-centred care, human connection, and local knowledge.
DiscussionThis study shows how futures-oriented, participatory methods can surface both the promise and the constraints of AI in rural healthcare. Successful implementation requires co-design with rural communities, equity-driven approaches, transparent governance frameworks, and investment in infrastructure and workforce capacity so that future technology adoption supports, rather than exacerbates, existing health disparities.

### 研究背景
人工智能(Artificial Intelligence, AI)可通过自动化监测、个性化护理与虚拟支持，重塑乡村医疗服务体系。但目前针对乡村场景下人工智能的未来发展路径仍有待深入探索。绝大多数人工智能应用均以城市为中心开发，未充分考量制约乡村医疗服务落地的基础设施瓶颈、从业队伍现状与社会文化动态特征。

### 研究方法
本研究调研了利益相关方对人工智能在乡村医疗领域未来角色的看法，明确了其应用的核心优先级、推动因素与阻碍因素。研究采用融合远景扫描与前瞻方法的参与式研究范式，在南澳大利亚州乡村卫生研究与教育会议的结构化研讨会中采集数据。本次研讨会共有40名参与者，涵盖全科医生、临床医师、医学生、研究人员与医疗行政人员，他们依次完成四项活动：历史事件映射、未来事件可能性推演、体验式未来场景构建，以及基于MoSCoW框架的优先级设定。研究人员对书面反馈进行系统性转录，并采用反思性主题分析法开展数据分析。

### 研究结果
本研究提炼出四大核心主题，涵盖利益相关方的优先级考量，以及他们认为未来技术整合所需的核心防护准则。这些主题围绕人工智能与技术部署为乡村及偏远地区医疗公平带来的机遇、以患者为中心的护理模式、伦理挑战，以及资金与体系性问题展开。参与者认可人工智能能够降低地理壁垒、提升医疗服务可及性，但同时也对数据隐私、治理、文化适配性，以及技术加剧现有健康差距的风险表示担忧。在各项活动中，参与者均强烈倾向于支持而非替代人类临床医师的人工智能方案，并强调了维持以患者为中心的护理、人际互动与本土知识的重要性。

### 研究讨论
本研究证明，面向未来的参与式研究方法能够揭示人工智能在乡村医疗领域的发展潜力与现实制约。要实现人工智能在乡村医疗的成功落地，需与乡村社区开展协同设计、采用公平导向的方法、建立透明的治理框架，并加大基础设施与从业队伍能力建设的投入，确保未来的技术应用能够缓解而非加剧现有健康差距。