Data used in this study were obtained from the 2023 Canadian Digital Health Survey (CDHS) that was commissioned by Canada Health Infoway to assess Canadians’ experiences and perceptions regarding digital health services, including the use of AI in health care [29,30]. The web-based survey was administered by Leger, one of Canada’s leading market research firms, between November 28 and December 28, 2023. Participants, aged 16 years and older, were recruited from Leger Opinion’s nationally representative online panel using computer-assisted web interviewing technology. A total of 10,130 respondents participated in the survey, which was available in both English and French.

The 2023 Canadian Digital Health Survey obtained informed consent from its 10,130 participants and adhered to the public opinion research standards of the Canadian Research and Insights Council and the global ESOMAR (European Society for Opinion and Marketing Research) network to ensure methodological rigor and data quality [29,31]. Information about respondents was deidentified and anonymized to protect privacy and confidentiality. Patients provided consent for data collection and evaluation.

The survey assessed four key variables related to patients’ perceptions of AI in health care using 4-point ordinal scales. To evaluate participants’ understanding of AI, they were asked to rate their knowledge on a scale from 1 (not at all knowledgeable) to 4 (very knowledgeable). The question “How comfortable are you with AI being used as a tool in health care?” was used to assess participants’ comfort level on a scale ranging from 1 (very uncomfortable) to 4 (very comfortable). A similar approach was used to assess attitudes toward the use of personal health data in AI research. Participants were asked how comfortable they felt about scientists using their personal health data for AI research when informed consent was provided, using the same 4-point scale. To examine privacy concerns, the survey also asked about comfort levels regarding AI research using deidentified health data without explicit consent. Participants were also asked about their comfort levels in applying AI in 7 areas: monitoring and predicting health conditions, decision support for health care professionals, precision medicine, drug and vaccine development, disease monitoring at home, tracking epidemics, and optimizing health care workflows.

Participants were asked to self-report any serious or chronic health conditions diagnosed by a health professional. The survey defined chronic illness as a condition expected to last, or already lasting, 6 months or more. Respondents could choose from a predefined list of 15 chronic conditions: chronic pain, cancer, diabetes, cardiovascular disease, Alzheimer’s disease, developmental disabilities, obesity, mental health conditions, and physical or sensory disabilities. Additionally, participants had the option to specify any other chronic illness not listed or indicate no chronic illness. We calculated a composite score representing the total number of chronic conditions reported by each respondent.

Digital health literacy was assessed using 8 items from eHealth Literacy Scale [32], which measures the ability to find, evaluate, and use health information on the internet. Items were rated on a 5-point Likert scale (1=strongly disagree to 5=strongly agree). After confirming their convergence and reliability using exploratory principal component analysis with varimax rotation (which extracted one factor, confirming unidimensionality) and Cronbach α (0.934), responses were summed to create a digital health literacy score, reflecting a respondent’s overall proficiency in navigating and utilizing online health resources. The following sociodemographic variables were also captured in the survey: age, sex, annual household income, citizenship, race, educational attainment, and employment status.

CDHS collected data from 10,130 Canadian adults on a broad range of topics related to digital health. Given this study’s focus on AI use in health care, only 6904 respondents who provided complete responses to AI-related questions were included in the analytic sample.

To assess potential selection or nonresponse bias, χ² tests were conducted to compare included and excluded respondents across all the sociodemographic variables: age, sex, income, education, race, employment, and citizenship. The χ² tests revealed significant differences between the overall respondents and our analytic sample across five variables (age, sex, employment, education, and income; P<.05), with our analytic sample overrepresenting males (53.2% vs 48.6%), individuals aged 25‐54 years (51.8% vs 48.6%), higher household incomes (35.7% above CAD 100,000 vs 33.6%), higher education levels (eg, graduate college or above: 39.7% vs 37.4%), and employed respondents (61.6% vs 58.6%).

To address this bias, we derived inverse probability weights (IPW) to adjust for the likelihood of inclusion in the analytic sample. The IPW values were estimated using a logistic regression model predicting inclusion based on sociodemographic variables. These weights were then combined with the original CDHS survey design weights (which account for sampling and nonresponse at the national level) to create a composite total weight. This combined weighting approach ensured that both survey design and sample selection bias were accounted for in all weighted analyses.

All statistical analyses were conducted using STATA 18 software. Descriptive statistics summarized respondent characteristics. To evaluate potential selection bias, the IPW was derived as described above.

Ordinal logistic regression models were estimated for four AI-related attitudinal outcomes: (1) knowledge of AI in health care, (2) comfort with use of AI in health care, (3) comfort with use of personal health data for AI with consent, and (4) comfort with use of personal health data for AI without consent. Each model was estimated three ways: unweighted, nonresponse-adjusted weighted (IPW), and fully weighted (combining IPW and CDHS-provided survey design weights) to evaluate robustness. All weighted models were estimated using survey (svy) commands in STATA to account for the complex survey design. Potential multicollinearity among predictors was assessed using Cramer’s V for categorical variables and variance inflation factors from proxy linear regressions.

To our knowledge, this is one of the first few studies to examine public attitudes toward use of AI in health care, with specific focus on the influence of sociodemographic, digital health literacy, and health-related factors. Overall, study respondents reported mixed levels of knowledge about AI and a considerable proportion (42.39%) expressing discomfort with AI use in health care. When personal health data were used for AI solutions with consent, the proportion of individuals becoming comfortable with AI use increased (64.69%), and when AI applications used personal deidentified health data without consent, a higher proportion (52.61%) expressed discomfort. A relatively higher proportion of respondents expressed greater comfort when AI was used in nonclinical areas like tracking epidemics and for improving healthcare workflows.

We found significant variations in knowledge levels of AI and comfort levels pertaining to AI use in health care based on sociodemographic, digital literacy, and the number of health conditions. Our results indicate that men, noncitizens, higher-income respondents, and respondents with greater digital health literacy exhibited higher odds of reporting comfort with AI use in health care and with the use of personal health data for AI. Older adults (65+ y) demonstrated higher comfort with AI use in health care, while younger (25–34 y) and middle-aged (35–54 y) adults were less comfortable with AI using their personal data, especially without consent.

Compared to Asian-origin respondents, White and Other racial groups had significantly lower comfort levels across models, while Black or African-origin respondents were notably less comfortable, only when personal health data were used for AI applications without consent. This finding suggests that lower comfort among Black respondents is not a general discomfort with AI but rather a heightened sensitivity to nonconsensual data use, thus underscoring the critical importance of transparency and opt-in data policies to foster trust among minority groups. Our findings also indicated that noncitizens exhibited higher comfort levels with AI use in health care as compared to Canadian citizens. The observed higher comfort among Asian-origin and noncitizen respondents and lower comfort among Black respondents suggests that broader cultural or experiential factors may influence attitudes toward AI in health care. Future qualitative or mixed-methods studies are needed to explore these factors. Prior research has shown that historical experiences, prior exposure to technology [33], and privacy concerns shape levels of trust in health technologies, particularly among minority populations [34,35]. Additionally, there is also some evidence that suggests that willingness to share personal health data for AI use depends strongly on the institution collecting the data and its intended purpose [24].

While digital health literacy improved comfort levels with use of AI, we also found that individuals with multiple health conditions were more accepting of AI when personal health data use was consensual. Individuals with multiple chronic conditions may have greater familiarity with varied health technologies and tools like wearables due to their increased prevalence [36-39], promoting appreciation for AI’s potential in managing complex conditions.

One of the critical challenges in deploying AI solutions in health care is ensuring fairness and reducing algorithmic bias, which often arises from unrepresentative training datasets [40-42]. To build AI models that produce accurate, equitable, and generalizable outcomes, they must be trained on large, diverse, and high-quality datasets that reflect the complete range of patient demographics and health conditions [43]. Our findings point to the importance of placing explicit patient consent at the core of all efforts in developing AI solutions.

Health care institutions and policymakers must establish standardized protocols for obtaining patient consent for AI use, ensuring that data collection aligns with ethical and legal frameworks (eg, HIPAA [Health Insurance Portability and Accountability Act] in USA, PIPEDA [Personal Information Protection and Electronic Documents Act] in Canada, and GDPR [General Data Protection Regulation] in Europe) [44,45]. These policies must define whether patient data is being used for research, commercial development, or clinical decision-making. They must also clarify how long the data will be stored, who can access it, and whether patients have the right to withdraw consent at any time [46]. Without well-defined guidelines, the risk of unauthorized data usage and breaches increases, undermining public confidence in health care AI.

Even when patients provide consent, strong privacy protections must be in place, particularly when data are pooled across multiple health systems. There is a growing concern about deidentification and whether anonymized health data can still be reidentified using advanced AI techniques [47]. To mitigate these risks, health care institutions must implement privacy-preserving solutions, such as federated learning, where AI models are trained across decentralized data sources without transferring raw patient data [48,49]. Blockchain-based consent management can offer a secure way for patients to track and manage their data access, while strict data governance frameworks are essential to ensure AI developers use health data more responsibly.

This study shows that comfort with AI in health care is strongly influenced by sociodemographic factors and digital literacy. Individuals who trust AI and understand how it works are more likely to support AI-driven health care applications, while those with privacy concerns or lower digital literacy may resist AI use in health care, specifically when personal health data is used without consent. Addressing these concerns through educational initiatives, transparent policies, and patient engagement strategies can help build public confidence in AI solutions in health care.

Our findings also indicate that respondents were significantly less comfortable with AI use when personal health data were used without explicit consent. This highlights a crucial ethical dilemma—even if deidentified, patient data still carries risks if used without oversight or patient involvement. Future research and policy discussions should explore how much control patients should have over their deidentified data, what level of transparency AI developers must provide to patients, and how AI models trained on patient data should be evaluated for fairness and accountability. Algorithmic bias, as seen in lower comfort among certain racial groups like African Americans, especially when consent is absent, could exacerbate health care disparities if AI models are trained on unrepresentative datasets [41]. Mitigating algorithmic bias through diverse dataset inclusion and continuous performance monitoring can help reduce disparities [50,51]. Accountability in clinical decision-making is critical to ensure that AI supports, rather than overrides, clinician judgment, while prioritizing patient autonomy in data use strengthens trust [52,53]. These ethical challenges highlight the need for aligning AI deployment with patient expectations and equitable outcomes.

To build trustworthy AI-enabled health care solutions, policymakers and health administrators need to design targeted public awareness campaigns, co-developed with patient advocates, that clearly explain AI’s clinical role and how it uses patient data [54]. In addition, implementing opt-in consent policies [55,56] can help alleviate patient fears about misuse of their data. Culturally tailored digital literacy programs can also help boost patient confidence about AI use in health care. Beyond patient engagement, standardized bias audits for all clinical AI tools [52], while establishing patient review boards or advisory committees to gather patient feedback and assess ethical implications before deployment can be effective [57].

This study has several important limitations. First, the cross-sectional design captures public attitudes at a single point in time. The survey was done at the end of 2023 in Canada, when new generational AI technologies were still emerging. As advances in AI technologies and public awareness evolve, attitudes may change, making longitudinal studies necessary. Second, the self-reported nature of the data may introduce bias. Respondents could have misestimated their AI knowledge and comfort levels due to social desirability. Additionally, self-reported digital literacy may not accurately reflect actual proficiency in digital health technologies. Third, the study explored respondent attitudes toward AI, without assessing whether they had any prior exposure to any AI health care tools, such as chatbots, etc. Fourth, though we had a fairly large sample size, it may not be representative of the general population, restricting the generalizability of results. Specifically, our sample included relatively small proportions of noncitizens (4.68%) and Black or African-origin respondents (3.66%), which reduces statistical power for these subgroups. Consequently, subgroup findings should be interpreted with caution. Fifth, our operationalization of chronic health conditions as a composite count is a measurement limitation. Different health conditions, based on their nature and severity, could influence varied levels of technological use and engagement. Future research could explore how specific health conditions, and their nature and severity, influence attitudes toward AI. We also acknowledge possible nonresponse bias, as over 3226 cases were removed due to missing answers on AI-related questions. This reduction in analytic sample size may have excluded individuals with systematically different attitudes toward AI. Although weighting adjustments were used to correct for this, some bias may remain, as the weighting cannot account for unobserved factors. For instance, respondents who systematically avoided answering AI-related questions may hold strong, unmeasured attitudes such as anxiety or mistrust toward AI, which could bias our analytic sample. Finally, while the study examined broad sociodemographic and health factors, it did not delve into more specific determinants of AI trust, such as ethical concerns, data security apprehensions, or past experiences with health care technologies. Future research could explore these in greater detail to better understand the specific reasons behind patient acceptance of AI in health care. Additional investigation through qualitative or mixed-method studies could throw light on specific nuances that shape the patient attitudes toward AI use in health care.

In conclusion, this study documents moderate levels of knowledge and comfort levels of the public regarding AI use in health care. Further, it highlights how sociodemographic characteristics, digital literacy, and health conditions are associated with public knowledge and comfort levels regarding AI use in health care. Our findings suggest significant socioeconomic disparities around the comfort levels with AI use in health care, while concerns persist around AI use without patient consent. These findings highlight the importance of transparent policies, patient education, and ethical data governance to improve public trust in AI-driven health care.