Accepted for/Published in: JMIR Formative Research
Date Submitted: Apr 23, 2024
Date Accepted: Aug 20, 2024
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Perceptions towards using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: A Qualitative Exploration of Māori Views
ABSTRACT
Background:
Asthma is a significant global health issue, impacting over 500,000 individuals in New Zealand and disproportionately affecting Māori communities in New Zealand, who experience worse asthma symptoms and attacks. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) models, are increasingly popular for prediction in the area of asthma. However, these AI models may under-represent minority ethnic groups and introduce bias, potentially exacerbating disparities.
Objective:
This study aims to explore the views and perceptions that Māori have toward using AI and ML technologies for asthma self-management, identify key considerations for developing asthma attack risk prediction models, and how to ensure Māori are represented in ML models without worsening existing health inequities.
Methods:
Semi-structured interviews were conducted with 20 Māori participants with asthma - three males and 17 females, aged 18 to 76 years. All the interviews were conducted one-on-one except for one interview that was conducted with 2 participants. Altogether, ten online interviews were performed, while the rest were kanohi ki te kanohi (face-to-face).
Results:
We identified four key themes: 1) Concerns about AI use, 2) Interest in using technology to support asthma, 3) Desired characteristics of AI-based systems and 4) Experience with asthma management. AI was relatively unfamiliar to participants, and they expressed concerns about trusting technology due to the previous history of colonisation but were interested in using technology to support their asthma management. We gained insights into user preferences regarding computer-based healthcare applications. Participants discussed their experiences, highlighting problems with healthcare quality and limited access to resources.
Conclusions:
The exploration revealed that there is a need for greater information about AI and technology for Māori communities, and trust issues relating to the use of technology. Expectations in relation to computer-based applications for health purposes were expressed. The research outcomes will inform future investigations on AI and technology to enhance the health of people with asthma, in particular those designed for indigenous populations in New Zealand.
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