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

2026-04-30

AI Summary

This study addresses a critical gap in the literature by examining consumer attitudes toward concrete applications of AI in healthcare, an area previously dominated by abstract conceptual discussions that hinder effective patient-facing tool deployment. Employing a mixed-methods approach, the research surveyed 275 Australian participants and incorporated a blinded experiment comparing AI-generated and physician-written clinical summaries. Findings reveal that while users express moderate optimism toward AI and acknowledge its perceived ease of use and usefulness, they harbor significant concerns regarding accuracy and data privacy. Notably, participants significantly preferred AI-generated summaries in blind testing yet struggled to correctly identify their source. The results underscore that users prioritize communication quality and visible human oversight over raw technical performance, offering vital empirical guidance for the design and implementation of AI-enabled health tools.

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.

Problem

• consumer attitudes
• healthcare AI
• AI acceptance
• digital health
• scenario-based evaluation

Innovation

• mixed-methods survey
• scenario-based evaluation
• consumer attitudes
• AI-generated consultation summary
• clinically supervised deployment