Listening to Patients Voices: Examining Public Attitudes towards Use of AI in Healthcare (Preprint)

Abstract

<sec> <title>BACKGROUND</title> 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. </sec> <sec> <title>OBJECTIVE</title> 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 </sec> <sec> <title>METHODS</title> 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. </sec> <sec> <title>RESULTS</title> 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&lt;0.001), male gender (OR: 1.55, p&lt;0.001), and higher income (OR: 1.22, p&lt;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. </sec> <sec> <title>CONCLUSIONS</title> 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. </sec>

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