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Lookup NU author(s): Dr Jeffry Hogg
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 Informa UK Limited, trading as Taylor & Francis Group. Introduction: Demand in clinical services within the field of ophthalmology is predicted to rise over the future years. Artificial intelligence, in particular, machine learning-based systems, have demonstrated significant potential in optimizing medical diagnostics, predictive analysis, and management of clinical conditions. Ophthalmology has been at the forefront of this digital revolution, setting precedents for integration of these systems into clinical workflows. Areas covered: This review discusses integration of machine learning tools within ophthalmology clinical practices. We discuss key issues around ethical consideration, regulation, and clinical governance. We also highlight challenges associated with clinical adoption, sustainability, and discuss the importance of interoperability. Expert opinion: Clinical integration is considered one of the most challenging stages within the implementation process. Successful integration necessitates a collaborative approach from multiple stakeholders around a structured governance framework, with emphasis on standardization across healthcare providers and equipment and software developers.
Author(s): Taribagil P, Hogg HDJ, Balaskas K, Keane PA
Publication type: Review
Publication status: Published
Journal: Expert Review of Ophthalmology
Year: 2023
Volume: 18
Issue: 1
Pages: 45-56
Online publication date: 07/02/2023
Acceptance date: 30/01/2023
ISSN (print): 1746-9899
ISSN (electronic): 1746-9902
Publisher: Taylor and Francis Ltd.
URL: https://doi.org/10.1080/17469899.2023.2175672
DOI: 10.1080/17469899.2023.2175672
