The Future of AI in Ophthalmology: How AI-Enabled Ocular Scans Can Predict Systemic Diseases and Aging

EasyChair Preprint 14474

Abstract

The integration of artificial intelligence (AI) into ophthalmology heralds a transformative era for both the diagnosis and prognosis of ocular and systemic diseases. This abstract explores the potential of AI-enabled ocular scans to revolutionize healthcare by predicting systemic conditions and aging processes with unprecedented accuracy. Ocular scans, particularly retinal imaging, offer a unique window into systemic health due to the retina's direct connection to the central nervous system and its role as a microvascular network. By leveraging deep learning algorithms and advanced image processing techniques, AI can analyze subtle changes in retinal structure and vascular patterns, identifying biomarkers associated with conditions such as diabetes, cardiovascular diseases, neurodegenerative disorders, and even aging itself.

This predictive capability extends beyond traditional diagnostic approaches, allowing for earlier intervention and personalized treatment plans. AI's ability to process large datasets and detect patterns not discernible to the human eye makes it an invaluable tool in pre-symptomatic diagnosis. For instance, AI can detect early signs of diabetic retinopathy or predict the risk of stroke by analyzing retinal blood flow patterns. Additionally, the application of AI in monitoring age-related macular degeneration (AMD) provides insights into the aging process, offering potential strategies for delaying or mitigating age-related decline.

Keyphrases: AI in Ophthalmology, Age-related Macular Degeneration (AMD), Diabetic Retinopathy, Ocular Scans, Systemic diseases, predictive diagnostics, retinal imaging

@booklet{EasyChair:14474,
  author    = {Anthony Collins},
  title     = {The Future of AI in Ophthalmology: How AI-Enabled Ocular Scans Can Predict Systemic Diseases and Aging},
  howpublished = {EasyChair Preprint 14474},
  year      = {EasyChair, 2024}}