Abstract
Presentation Description :
In recent years, artificial intelligence (AI) has significantly transformed the field of ophthalmology, especially in remote areas where access to specialized healthcare services is limited. AI-driven screening tools have emerged as a game-changer, providing an effective and efficient means of diagnosing and managing various eye conditions. Equipped with advanced algorithms and deep learning capabilities, AI-enabled screening tools in ophthalmology can swiftly analyze retinal images, identify potential abnormalities, and accurately detect a spectrum of eye diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration. These technologies not only aid in early disease detection but also enable timely intervention, reducing the risk of irreversible vision impairment and blindness. The integration of AI-based screening in remote areas has proven instrumental in overcoming the shortage of ophthalmic specialists. Local healthcare workers, equipped with portable retinal imaging devices, can capture high-quality images of patients' eyes. These images are then transmitted to centralized AI systems, which swiftly analyze them and generate detailed reports, providing healthcare professionals with timely and accurate diagnostic insights. Furthermore, AI-powered telemedicine platforms have facilitated remote consultations, enabling ophthalmologists to provide expert guidance and recommendations to healthcare workers and patients in distant locations. This collaborative approach has revolutionized the delivery of eye care services, ensuring that individuals in underserved communities receive prompt and effective treatment, thereby minimizing the burden of preventable visual impairments and enhancing overall quality of life. As AI continues to evolve, its integration in ophthalmic screening promises to bridge the gap between urban and remote healthcare settings, fostering a more equitable and accessible eye care landscape for individuals in underserved regions worldwide.
This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.