ArticleFrontiers in digital health2026
Exploring plausible futures for artificial intelligence in rural healthcare: insights from participatory foresight methods.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Artificial 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. Methods: This 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. Results: Four 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. Discussion: This 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.
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