Influencing public acceptance of artificial intelligence (AI) in healthcare delivery

Despite the potential of artificial intelligence (AI) to transform healthcare delivery and reduce costs, adoption remains uneven across populations. Understanding the demographic, behavioral, and cognitive factors influencing public willingness to use AI-powered health tools is critical for equitable implementation. This study examined determinants of AI adoption in healthcare among adults in the United States (U.S.).

A cross-sectional survey was conducted between March and June 2024 using convenience sampling across the U.S. The study included 568 adult respondents recruited via Qualtrics. The survey assessed demographic characteristics, digital health behaviors, self-reported health status, cognitive and attitudinal factors, and behavioral intentions related to AI use in healthcare. Logistic regression models were used to examine associations between predictors and willingness to adopt AI, with z-tests for subgroup comparisons and Bonferroni correction applied for multiple hypothesis testing.

The sample was predominantly female (66.7%) and Hispanic/Latino (50.7%), with moderate income and education levels. Older age was negatively associated with AI adoption (β = -0.029), males were less likely to use AI than females (β = -0.388), and income was positively correlated with AI adoption (β = 0.096). Trust in AI was substantially lower than trust in physicians: 14.6% trusted ChatGPT's diagnosis for serious illness compared with 92.3% trusting physicians, and 17.1% versus 96.4% for specialist referrals. Telehealth use strongly predicted AI adoption (β = 1.012), while lower self-rated mental health was associated with higher AI use (β = -0.254). Uninsured participants reported higher trust in AI diagnostic capabilities than insured participants (57% vs. 43%, p < 0.05). Ethnic differences were observed, with Asian participants reporting higher AI usage rates than Hispanic participants (16.49% vs. 5.56%, p < 0.05).

AI adoption in healthcare is shaped by the interaction of demographic, socioeconomic, and cultural factors. While AI has the potential to expand healthcare access, adoption patterns reflect existing disparities in healthcare access and trust. Trust emerged as a central determinant, with AI functioning as a compensatory tool when traditional healthcare access is limited. Given the U.S.-specific context, findings should be interpreted as exploratory and may not generalize to other healthcare systems. These results highlight the need for future research on transparency, digital literacy, and structural barriers to support equitable implementation of healthcare AI.