Artificial Intelligence in Optometry: Current and Future Perspectives.

Abstract

With the global shortage of eye care professionals and the increasing burden of vision impairment, particularly in low- and middle-income countries, there is an urgent need for innovative solutions to bridge gaps in eye care services. Advances in artificial intelligence (AI) over recent decades have significantly impacted healthcare, including the field of optometry. When integrated into optometric workflows, AI has the potential to streamline decision-making processes and enhance system efficiency. To realize this potential, it is essential to develop AI models that can improve each stage of the patient care workflow, including screening, detection, diagnosis, and management. This review explores the application of AI in optometry, focusing on its potential to optimize various aspects of patient care. We examined AI models across key areas in optometry. Our analysis considered crucial parameters, including model selection, sample sizes for training and validation, evaluation metrics, and the explainability of the models. This comprehensive review identified both the strengths and weaknesses of existing AI models. The majority of image-based studies utilized CNN or transfer learning models, while clinical data-based studies primarily employed RF, SVM, and XGBoost. In general, AI models trained on large datasets achieved higher accuracy. However, many optometry-focused models faced limitations due to insufficient sample sizes-28% of studies were trained on fewer than 500 samples, 18% used fewer than 200 samples, and over half validated their models on fewer than 500 samples, with 38% validating on fewer than 200. Additionally, some studies that used the same data for both training and validation experienced overfitting, leading to reduced accuracy. Notably, 20% of the included studies reported accuracy below 80%, limiting their practical applicability in clinical settings. This review provides optometrists with valuable insights into the strengths and weaknesses of AI models in the field, aiding in their informed implementation in clinical settings.