Currently submitted to: Journal of Medical Internet Research
Date Submitted: May 27, 2026
Open Peer Review Period: May 28, 2026 - Jul 23, 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Warning: This is an author submission that is not peer-reviewed or edited.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.
Patient Perspectives on Use of Artificial Intelligence in Clinical Practice: A Narrative Review
ABSTRACT
Patient perspectives on artificial intelligence (AI) use in healthcare have not been well studied. Yet, this understanding is crucial for ensuring these perspectives are addressed in AI development and deployment in clinical settings. This review aims to synthesize existing literature and identify key themes regarding patient perspectives on AI. The electronic search strategy sourced 351 studies from five databases: Medline ProQuest, Ovid Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, and Google Scholar. Ultimately, 20 studies were included in this review, along with four additional sources from grey literature. Key themes that emerged across these studies included: 1. the inability of AI to replace physicians, 2. human authority in high-stakes clinical situations, 3. lack of education relating to AI, 4. mistrust of AI, 5. the need for greater AI transparency. Importantly, patient concerns within these categories exhibited remarkable heterogeneity, which reinforces the need for flexible AI tools that address the diverse needs of their patients. Despite these concerns, patient consensus overwhelmingly favoured the inclusion of AI in healthcare as a tool for physician support. Consensus stemmed from patients’ hope for the AI-supported clinician of the future to be the ‘ideal physician’. This paper is intended to serve as a practical guide to aid healthcare policymakers’ and practitioners’ understanding of patient perspectives regarding AI in healthcare. Only once this understanding has been achieved can AI technologies truly reach their full potential.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.