Comprehensive Summary
This study highlights the increasing success of Artificial Intelligence in diagnosing and predicting the prognosis of ophthalmological diseases. It focuses on multiple studies, including AI-based algorithms, retinal images, fundus and optic nerve head (ONH) photographs, and expert reviews. It also investigates artificial intelligence role in anterior segment diseases like keratoconus (KCN) and dry eye syndrome (DES), as well as neuro-ophthalmological conditions, including papilledema and optic nerve disorders. The findings indicate that AI can significantly improve the speed, accuracy, sensitivity, and specificity of diagnosing both ophthalmological and systemic diseases detectable through ocular imaging. The authors emphasize the need for collaboration between medicine and engineering to advance AI predictive capabilities while addressing ethical, social, and legal considerations.
Outcomes and Implications
This study is important because it highlights the ability of AI to revolutionize ophthalmic practice, offering faster and more accurate diagnoses of a wide range of conditions, thereby potentially preventing vision loss and improving patient outcomes. AI can detect early DR through OCT angiography (OCTA) with high accuracy, allowing for timely treatment. In glaucoma, AI algorithms can analyze fundus images to identify at-risk individuals, aiding in early diagnosis and management. For AMD, AI-powered imaging can improve the detection of macular degeneration, enabling timely intervention to slow disease progression. Additionally, AI aids in diagnosing anterior segment diseases such as KCN, allowing for earlier surgical interventions. Further studies are needed to validate these models in diverse populations.