Machine Learning Innovations in Ophthalmology

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in ophthalmology has significantly advanced diagnostic precision and patient care. Leveraging diverse datasets such as Electronic Health Records (EHRs), Optical Coherence Tomography (OCT) images, and genomic data, AI-driven approaches have proven effective in diagnosing eye conditions and systemic diseases with ocular manifestations. This study reviews various applications of AI in ophthalmology, including fungal keratitis, diabetic retinopathy, glaucoma, and rare genetic disorders. Techniques such as Lasso regression, deep transfer learning, and Random Forest analysis have been employed to enhance diagnostic models and improve prediction accuracy. For example, deep transfer learning models like VGG19 and DenseNet have demonstrated superior performance in identifying diabetic retinopathy from OCT scans. Additionally, AI’s application in genomic studies has shown promising results in detecting genetic markers for rare diseases. The contributions of these studies extend beyond clinical applications, emphasizing AI’s role in personalized medicine, early disease detection, and improved treatment planning. By validating models across multiple centers, the scalability and consistency of AI solutions in real-world clinical environments are reinforced. This review underscores the transformative potential of AI and ML in shaping the future of ophthalmology, fostering more accurate diagnoses and personalized treatment strategies.