Public evaluations of AI-mediated healthcare when considering people with diverse access and support needs: a...

Artificial intelligence (AI) is increasingly being introduced into healthcare, including patient communication, monitoring, triage, decision support, and care-related services. Accessibility is central to these developments, particularly when AI-mediated healthcare is considered in relation to people with diverse access and support needs.

This study examined how accessibility-related evaluations of AI in healthcare are patterned when respondents are prompted to consider people with diverse access and support needs, and how respondents describe risks and conditions of acceptable AI-mediated care in this context.

We conducted a mixed-methods questionnaire study with a panel sample (N = 1,151). Quantitative analyses combined Bayesian regression, confirmatory factor analysis, and correlation analysis to examine accessibility-related evaluations of healthcare AI. Qualitative analysis examined open-ended concern elaborations from a subgroup of respondents (n = 117) using codebook thematic analysis with collaborative coding.

Support-oriented evaluations clustered together more strongly than they aligned with concern. In the selected regression models, technological acceptance and age were the clearest correlates: higher technological acceptance was associated with more supportive evaluations, while older age was associated with greater concern and lower endorsement of AI's supportive potential in some models. Four qualitative themes were identified: Human Recourse and Relational Care; Communicative Accessibility and Misunderstanding; Safety, Reliability, and Override Capacity; and Privacy, Misuse, and Unfair Classification. Respondents articulated concern through questions of relational care, communicative fit, accountability, fairness, safety, and access to human support when AI systems fail or are misunderstood.

The findings suggest that healthcare AI implementation should address intelligibility, communicative accessibility, bounded system roles, accountability, and human recourse as central conditions for acceptable AI-mediated care.