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Identifying key characteristics of developed artificial intelligence algorithms to achieve meaningful impact on
Daouda Coulibaly1,2, Azadeh Bayani1,2, Bry Sylla3
1Laboratoire Transformation Numérique en Santé, Montréal, Québec, Canada.
Introduction:
Empirical data on the barriers limiting artificial intelligence (AI)'s impact on healthcare are scarce, particularly within the Canadian context. This study aims to address this gap by conducting a scoping review to identify and evaluate AI algorithms developed by researchers affiliated with Canadian institutions for patient triage, diagnosis and care management. The goal is to identify characteristics in the developed AI algorithms that can be leveraged for a better impact.
Methods And Analysis:
A scoping review will be conducted following the JBI Methodology for Scoping Reviews and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. Relevant literature will be identified through comprehensive searches of MEDLINE (PubMed), CINAHL (EBSCO) and Web of Science (Clarivate) databases, combining keywords related to AI, clinical management and the Canadian context. Studies published after 2014, in English or French, that discuss AI algorithms developed for patient triage, diagnosis or care management by researchers affiliated with Canadian institutions will be included. Data from the selected articles will be extracted and analysed descriptively, and findings will be presented in tabular form accompanied by a narrative summary.
Ethics And Dissemination:
Ethical approval is not required for this study as it involves the review of publicly available literature. The scoping review is expected to be completed by November 2025. The findings will be disseminated through publications in peer-reviewed journals and presentations at conferences focused on AI and healthcare practice.
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