{"title":"Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities","authors":"P. Taribagil, H. J. Hogg, K. Balaskas, P. Keane","doi":"10.1080/17469899.2023.2175672","DOIUrl":null,"url":null,"abstract":"ABSTRACT 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.","PeriodicalId":39989,"journal":{"name":"Expert Review of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Review of Ophthalmology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17469899.2023.2175672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACT 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.
期刊介绍:
The worldwide problem of visual impairment is set to increase, as we are seeing increased longevity in developed countries. This will produce a crisis in vision care unless concerted action is taken. The substantial value that ophthalmic interventions confer to patients with eye diseases has led to intense research efforts in this area in recent years, with corresponding improvements in treatment, ophthalmic instrumentation and surgical techniques. As a result, the future for ophthalmology holds great promise as further exciting and innovative developments unfold.