Perceptions Toward Using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: Qualitative Exploration of Māori Views
Date
Authors
Widana Kankanamge, Darsha
Mirza, Farhaan
Bidois-Putt, Marie-Claire
Naeem, M Asif
Chan, Amy Hai Yan
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
JMIR Publications
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 asthma risk prediction. However, these AI models may underrepresent minority ethnic groups and introduce bias, potentially exacerbating disparities. OBJECTIVE: This study aimed 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 ensure Māori are represented in ML models without worsening existing health inequities. METHODS: Semistructured interviews were conducted with 20 Māori participants with asthma, 3 male and 17 female, aged 18-76 years. All the interviews were conducted one-on-one, except for 1 interview, which was conducted with 2 participants. Altogether, 10 web-based interviews were conducted, while the rest were kanohi ki te kanohi (face-to-face). A thematic analysis was conducted to identify the themes. Further, sentiment analysis was carried out to identify the sentiments using a pretrained Bidirectional Encoder Representations from Transformers model. 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 and opportunities for technology to improve care. AI was relatively unfamiliar to many participants, and some of them expressed concerns about whether AI technology could be trusted, kanohi ki te kanohi interaction, and inadequate knowledge of AI and technology. These concerns are exacerbated by the Māori experience of colonization. Most of the participants were interested in using technology to support their asthma management, and we gained insights into user preferences regarding computer-based health care applications. Participants discussed their experiences, highlighting problems with health care quality and limited access to resources. They also mentioned the factors that trigger their asthma control level. CONCLUSIONS: The exploration revealed that there is a need for greater information about AI and technology for Māori communities and a need to address 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.Description
Keywords
artificial intelligence, asthma risk prediction, health system development, machine learning, mobile phone, māori perceptions, 4203 Health Services and Systems, 42 Health Sciences, Data Science, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Bioengineering, Lung, Asthma, 7.1 Individual care needs, Respiratory, 3 Good Health and Well Being, 32 Biomedical and clinical sciences, 42 Health sciences
Source
JMIR Formative Research, ISSN: 2561-326X (Print); 2561-326X (Online), JMIR Publications, 8, e59811-. doi: 10.2196/59811
DOI
Publisher's version
Rights statement
© Widana Kankanamge Darsha Jayamini, Farhaan Mirza, Marie-Claire Bidois-Putt, M Asif Naeem, Amy Hai Yan Chan. Originally published in JMIR Formative Research (https://formative.jmir.org), 30.10.2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.