Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 14, 2025
Open Peer Review Period: May 15, 2025 - Jul 10, 2025
Date Accepted: Nov 11, 2025
(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Warning: This is an author submission that is not peer-reviewed or edited.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.
Listening to Patients Voices: Examining Public Attitudes towards Use of AI in Healthcare
ABSTRACT
Background:
Artificial intelligence (AI) holds great promise in transforming healthcare delivery. However, successful implementation of AI projects in healthcare depends on patients' acceptance and trust. There is only limited empirical research examining public perceptions, particularly on the use of personal health data in AI applications in healthcare.
Objective:
To examine public knowledge and comfort levels with AI use in healthcare, including use of personal health data with and without consent, and to assess how sociodemographic factors, digital literacy, and health conditions influence these perceptions
Methods:
We analyzed data from 6,904 Canadian adults who participated in the 2023 Canadian Digital Health Survey. AI-related knowledge and comfort levels were measured using ordinal scales. Sociodemographic characteristics, digital health literacy (assessed through an 8-item scale), and self-reported chronic health conditions were included as predictors. Ordinal logistic regression models were used to assess associations between these factors and AI-related attitudes.
Results:
42.3% of respondents reported moderate AI knowledge, while only 7.8% were very knowledgeable. Overall, 44.6% were comfortable with AI in healthcare, increasing to 64.7% when data was used with consent, but decreasing when used without consent (52.6% uncomfortable). Respondents were most comfortable with AI for epidemic tracking and healthcare workflows, and less so for clinical tasks. Higher digital health literacy (OR: 1.03, 95% CI: 1.03–1.04, p<0.001), male gender (OR: 1.55, p<0.001), and higher income (OR: 1.22, p<0.001) were significantly associated with greater AI knowledge. Older adults (65+), men, non-citizens, and individuals with multiple chronic conditions were more comfortable with AI in healthcare. Racial differences were evident, with White and "Other" racial groups exhibiting lower comfort levels with AI compared to Asian-origin respondents, while Black/African respondents were significantly less comfortable when personal data was used without consent.
Conclusions:
The findings point to enhancing transparent policies, digital literacy, and ethical data governance as key to increasing public trust in AI-driven healthcare. Addressing privacy concerns and ensuring equitable AI integration can improve acceptance and patient engagement.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.