Integrating artificial intelligence with electronic health records can model the risk of diabetic retinopathy progression, but challenges in data quality, privacy, and informed consent remain.
The goal of personalized diabetes eye care is to accurately predict in real-time the risk of diabetic retinopathy (DR) progression and visual loss. The use of electronic health records (EHR) provides a platform for artificial intelligence (AI) algorithms that predict DR progression to be incorporated into clinical decision-making. By implementing an algorithm on data points from each patient, their risk for retinopathy progression and visual loss can be modeled, allowing them to receive timely treatment. Data can guide algorithms to create models for disease and treatment that may pave the way for more personalized care. Currently, there exist numerous challenges that need to be addressed before reliably building and deploying AI algorithms, including issues with data quality, privacy, intellectual property, and informed consent.
Jacoba et al. (Thu,) conducted a review in Diabetic retinopathy. Artificial intelligence algorithms integrated with electronic health records was evaluated on Prediction of diabetic retinopathy progression and visual loss. Integrating artificial intelligence with electronic health records can model the risk of diabetic retinopathy progression, but challenges in data quality, privacy, and informed consent remain.
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