Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning

Table 4 Artificial intelligence-driven personalized management strategies in diabetic retinopathy

AI application	Ref.	Description	Impact on patient care
Predicting disease progression	Kong and Song[73], 2024	Analyse vast and diverse datasets, including retinal images, genetic information, blood glucose levels, and other patient-specific variables, to identify subtle patterns and predict the likelihood of disease advancement with higher accuracy	Allows for timely intervention and personalized treatment plans
Optimizing treatment regimens	Patibandla et al[74], 2024	Analyse patient data to predict the effectiveness of different treatment options, such as laser therapy or anti-VEGF injections, and recommend the most suitable approach for each individual	Ensures patients receive the most effective treatments based on individual data
Personalizing follow-up schedules	Silva et al[75], 2024	Determine the optimal frequency of eye exams and other monitoring measures, ensuring timely detection of any changes in a patient's condition	Helps in timely detection of changes in the patient's condition

AI: Artificial intelligence.