Currently submitted to: JMIR Formative Research
Date Submitted: Nov 2, 2026
Open Peer Review Period: Nov 3, 2026 - Dec 29, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited.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.
Public Enthusiasm for Artificial Intelligence in Medicine by Income, Insurance Status, and Healthcare Need: A Nationally Representative Survey
ABSTRACT
Background:
Deployment of artificial intelligence (AI) in healthcare is expanding rapidly, yet little is known about how public expectations of AI-enabled medical benefits are distributed across socioeconomic groups. Prior survey research has documented aggregate enthusiasm but has not examined how income, insurance status, or health burden shape who expects to benefit, or who does not.
Objective:
To examine whether reported enthusiasm for AI medical advances is associated with healthcare need (age, health status) and expected access to healthcare innovation (insurance status, income), and to assess whether patterns of lower enthusiasm among constrained-access populations are consistent with anticipatory resignation about inequitable diffusion.
Methods:
We analyzed data from the Spring 2026 RAND American Life Panel (N=2,018), a nationally representative probability-based survey of U.S. adults. The outcome was selection of "medical advances and better healthcare" from a structured list of anticipated AI benefits. Survey-weighted logistic regression (N=2,009) adjusted for age, self-rated health, household income, insurance status, AI usage frequency, sex, and race/ethnicity. A spline model tested whether income moderated the age gradient in enthusiasm.
Results:
Medical advances was the most commonly anticipated AI benefit (46%). Adults aged 65 and older were more than twice as likely as younger adults to select medical AI benefits (adjusted odds ratio [aOR] 2.09, 95% CI 1.59-2.73, P<.001). Adults in fair health were 65% more likely than those in excellent health (aOR 1.65, 95% CI 1.00-2.71, P=.048). Lower income and lack of insurance were independently associated with reduced enthusiasm (income ≥$150,000 vs under $50,000: aOR 1.71, 95% CI 1.21-2.43, P=.003; uninsured vs insured: aOR 0.53, 95% CI 0.29-0.94, P=.031). The income gap in predicted probability widened to approximately 30 percentage points by the early 70s.
Conclusions:
Reported enthusiasm for AI in medicine is highest among those with the greatest healthcare need and lowest among those with the most constrained access to innovation. Coverage and deployment decisions will determine whether those expectations are confirmed or contradicted.
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