Patient Experiences of AI-Assisted Diabetic Retinopathy Screening: Trust, Consent and Understanding of Results

Zhang Shuixin; Ali Napiah Nasution; Clarisa Lister

doi:10.61919/mx93my14

Authors

Zhang Shuixin Universitas Prima Indonesia, Indonesia Author
Ali Napiah Nasution Universitas Prima Indonesia, Indonesia Author
Clarisa Lister Universitas Prima Indonesia, Indonesia Author

DOI:

https://doi.org/10.61919/mx93my14

Abstract

Background: Artificial intelligence (AI)-assisted diabetic retinopathy screening can increase the speed and accessibility of retinal image interpretation, particularly in primary-care and underserved settings. However, diagnostic performance alone does not establish whether patients understand AI involvement, provide meaningful consent, trust automated decisions, or successfully navigate care after screening. Patient-centred evidence remains fragmented across studies of acceptance, satisfaction, trust, consent, data governance, communication, and referral. Objective: This critical integrative review synthesized patient-focused evidence on AI-assisted diabetic retinopathy screening to examine how patients develop or withhold trust in AI-supported screening, whether disclosure and consent processes support meaningful understanding and choice, and how patients comprehend results and navigate subsequent clinical care. Methods: Peer-reviewed literature published between January 2016 and July 2026 was identified using structured combinations of terms related to diabetic retinopathy, retinal screening, artificial intelligence, patient experience, trust, consent, privacy, understanding, communication, and referral, supplemented by citation chaining. Twenty publications informed the conceptual and theoretical context, while a separate corpus of 15 primary studies published between 2021 and 2026 informed the findings. Heterogeneous evidence from surveys, implementation studies, experiments, and patient-experience assessments was synthesized thematically, with design-sensitive critical appraisal. Results: Three interrelated themes emerged. Trust was conditional on visible human oversight and accountability, with patients generally preferring AI as an adjunct rather than an autonomous substitute for clinicians. Evidence concerning consent was substantially less developed than evidence concerning acceptance; disclosure did not necessarily establish comprehension, voluntariness, or permission for secondary data use. Speed, local access, and immediate results were valued, but favourable screening experiences did not consistently translate into referral completion or understanding of subsequent actions. Conclusion: Patient-centred AI-assisted diabetic retinopathy screening requires more than technical accuracy or high satisfaction. Sustainable implementation depends on identifiable human accountability, intelligible consent, practical explanation of results, transparent data governance, meaningful choice, and a navigable pathway from screening to follow-up care

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