Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions

Abstract

Background: Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review. Materials and Methods: We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease. Results: Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria. Conclusion: In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient’s access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.

Role of Machine Learning and Artificial Intelligence in the Diagnosis and Treatment of Refractive Errors for Enhanced Eye Care: A Systematic Review

“…Finally, optometrists have demonstrated a willingness to incorporate AI into their practice to increase their efficiency and improve the patient experience [ 19 ]. The combination of AI and ML algorithms using both structured and unstructured data has the potential to increase patient access to screening and clinical diagnosis while decreasing healthcare costs, particularly in communities facing financial shortages [ 20 ].…”

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification

“…The ability of convolutional neural networks (CNN) to recognize patterns in images for medical diagnosis has surpassed the accuracy of clinical specialists in experimental settings in various medical fields ( 2 ). To date algorithms have been successfully applied to image analysis for radiology ( 3 ), cardiology ( 4 ), ophthalmology ( 5 ), and dermatology ( 6 ). However, the translation of AI into clinical practice is still in its early stages, as researchers navigate complex issues relating to patient informed consent and privacy, representative and diverse training datasets, patient and clinician trust, explainable AI, and clinical workflow ( 7 ).…”

Recent evidence of economic evaluation of artificial intelligence in ophthalmology

“…Artificial intelligence in healthcare stemmed from the premise that well tolerated and effective use of its algorithms or tools are likely to have a complementary role to human cognition to improve health delivery [5]. Ophthalmology is the medical field wherein artificial intelligence has been studied extensively for assisting in preventing visual loss [6]. Despite the growing number of studies of artificial intelligence in healthcare and ophthalmology, only a handful are dedicated to HEE [7 ▪▪ ,8].…”