Consumer Attitudes Towards AI in Digital Health: A Mixed-Methods Survey in Australia

arXiv cs.AI / 5/1/2026

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Key Points

The study uses a mixed-methods survey of 275 Australians to assess readiness, acceptance, trust, and perceived risks of healthcare AI, finding moderate optimism and high perceived usefulness and ease of use.
Despite generally positive attitudes, participants expressed significant concerns about accuracy, safety, and how data would be used.
In a scenario-based comparison, participants strongly preferred AI-generated consultation summaries for quality, empathy, and overall usefulness, but they were almost unable to reliably identify which summary was produced by AI.
The results suggest consumers evaluate healthcare AI based on concrete communication quality and visible human oversight, highlighting the importance of clinically supervised deployment frameworks rather than relying on technical performance alone.

Abstract

AI applications are increasingly being introduced into digital health. While technical performance has advanced rapidly, successful deployment mainly depends on consumer attitudes, especially to patient-facing applications. However, most existing research examines consumer attitudes towards healthcare AI at an abstract level rather than in response to concrete artefacts. We report a mixed-methods survey study in Australia (N=275) examining consumer readiness, acceptance, trust, and risk perceptions of healthcare AI, combined with a scenario-based evaluation of an AI-generated versus clinician-written consultation summary. Participants expressed moderate optimism and strong perceived usefulness and ease of use, but also substantial concerns about accuracy, safety, and data use. In the scenario task, the AI-generated summary was strongly preferred for quality, empathy, and overall usefulness, yet identification of the AI summary was near chance. Findings show that consumers judge AI through concrete communication quality and visible human governance, underscoring the need for clinically supervised deployment frameworks beyond technical performance alone.