Jayamini, Widana Kankanamge DarshaMirza, FarhaanBidois-Putt, Marie-ClaireNaeem, M AsifChan, Amy Hai Yan2025-03-192025-03-192024-10-30European Respiratory Journal 2024 64(suppl 68): PA405; DOI: https://doi.org/10.1183/13993003.congress-2024.PA405http://hdl.handle.net/10292/18888Background: Asthma affects over half a million people in New Zealand with a disproportionate effect on Māori. Digital technologies including Artificial Intelligence (AI) and Machine Learning (ML) are increasingly used in health but may under-represent minority ethnic groups and introduce bias potentially exacerbating inequities. Aims: To explore Māori perceptions of using digital technology for asthma management, particularly AI for asthma attack prediction. Methods: Semi-structured interviews were conducted with Māori adults with asthma to explore perceptions, experiences, and expectations towards using AI and ML to predict asthma attacks. Interviews were analysed thematically. Results: Twenty Māori adults (3 males and 17 females) aged 18 to 76 years were recruited. Four key themes were identified: Concerns about AI; interest in using technology to support asthma; desired characteristics of AI-based systems; and experience in asthma management. AI was relatively unfamiliar to most and concerns were expressed about trusting technology. Participants were interested in using technology and AI but wanted more information to build trust and address concerns. There was a preference for face-to-face interaction at the start of and during technology use. Prior negative experiences with medical staff and accessibility issues to healthcare resources were identified barriers. Conclusion: Whilst concerns about AI were expressed, there was an interest in using AI and technology for asthma management. Access to information to build trust was key for technology uptake in Māori.© European Respiratory Society. Free.46 Information and Computing Sciences4602 Artificial IntelligenceNetworking and Information Technology R&D (NITRD)BioengineeringMachine Learning and Artificial IntelligenceAsthmaLungRespiratoryConference ContributionOpenAccess10.1183/13993003.congress-2024.pa405