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Lookup NU author(s): Dr Nehal HassanORCiD, Bob Slight, Kweku Bimpong, Dr Dan Weiand, Professor Graham MorganORCiD, Professor Sarah Slight
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2024.Artificial intelligence (AI)-enabled decision aids can contribute to the shared decision-making process between patients and clinicians through personalised recommendations. This systematic review aims to understand users’ perceptions on using AI-enabled decision aids to inform shared decision-making. Four databases were searched. The population, intervention, comparison, outcomes and study design tool was used to formulate eligibility criteria. Titles, abstracts and full texts were independently screened and PRISMA guidelines followed. A narrative synthesis was conducted. Twenty-six articles were included, with AI-enabled decision aids used for screening and prevention, prognosis, and treatment. Patients found the AI-enabled decision aids easy to understand and user-friendly, fostering a sense of ownership and promoting better adherence to recommended treatment. Clinicians expressed concerns about how up-to-date the information was and the potential for over- or under-treatment. Despite users’ positive perceptions, they also acknowledged certain challenges relating to the usage and risk of bias that would need to be addressed. Registration: PROSPERO database: (CRD42020220320)
Author(s): Hassan N, Slight R, Bimpong K, Bates DW, Weiand D, Vellinga A, Morgan G, Slight SP
Publication type: Article
Publication status: Published
Journal: npj Digital Medicine
Year: 2024
Volume: 7
Issue: 1
Online publication date: 21/11/2024
Acceptance date: 04/11/2024
Date deposited: 16/12/2024
ISSN (electronic): 2398-6352
Publisher: Nature Research
URL: https://doi.org/10.1038/s41746-024-01326-y
DOI: 10.1038/s41746-024-01326-y
Data Access Statement: All the data generated from this review are included in the manuscript and Supplementary material.
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