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.
Co-designing animated videos to explain large language models and their use in healthcare and research
IntroductionThe increasing development of large language models (LLM) in healthcare research is taking place without patient and public involvement and engagement (PPIE). Part of the