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Researchers have developed an AI algorithm that analyzes retinal images to estimate biological age, potentially aiding in early health risk detection. This method, based on the retinal age gap (RAG), offers a non-invasive way to assess aging and health status. The study involved over 29,000 retinal scans and demonstrated an average prediction error of 2.5-2.7 years for chronological age. Higher RAG values correlated with various health factors, indicating its potential as a biomarker for biological age. Future research is needed to validate these findings and enhance the model's accuracy.