Generative Artificial Intelligence for Retinal Degeneration: a Review

Every stratum of professionals has been impacted by the rapid improvements in technology. Artificial intelligence (AI) has had a genuinely unmatched impact on the fields of transportation, agriculture, meteorology, and health. We can now anticipate the future more accurately and confidently using AI using historical datasets. Although there are many applications for AI, this research focuses only on generative AI for the diagnosis of retinal disorders. The generator and the discriminator are the two primary models used in generative AI. While the discriminator tries to distinguish between accurate and false, the generator is employed to generate noisy random input. Together, these two models operate. For the treatment of retinal illnesses such as diabetic retinopathy, also known as DR, age-related macular degeneration, or AMD, and Retinal Fundus, commonly known as RF, ophthalmologists have used Generative AI. Based on the input datasets, generative AI may categorize them into specific groups by recognizing patterns in data. The subject of generative AI is relatively young, and numerous datasets are needed for it to gain experience as a stakeholder. Nevertheless, with every opportunity comes a challenge. The interpretation of technology-generated data that are outdated, technical difficulties, patient and physician approval of the algorithm, and its legal implications are further possible roadblocks for technology in ophthalmology. In a nutshell, yes there are challenges to the use of AI technology in diagnosing diseases related to ophthalmology, but these challenges can be solved. Hence, the future of Generative AI shows a bright picture in clinical development.