Core Viewpoint - The article discusses the development of a comprehensive biological clock model, LifeClock, which can accurately predict biological age across the entire lifespan, from infancy to old age, based on routine clinical data [6][19]. Group 1: Biological Age vs. Chronological Age - Biological age (BA) is a more accurate measure of an individual's aging process compared to chronological age (CA), as it reflects the accumulated biological damage relative to average individuals of the same actual age [9][12]. - The study highlights the existence of two distinct biological clock models: a "developmental clock" before age 18, which governs growth, and an "aging clock" after age 18, which governs functional decline [7][18]. Group 2: Research Findings and Methodology - The research utilized nearly 25 million clinical visit records to develop LifeClock, which predicts biological age and assesses its association with disease risk and survival outcomes [5][16]. - The AI model EHRFormer was trained using data from 9,680,764 individuals, allowing for high-precision analysis of developmental and aging dynamics [16][21]. Group 3: Implications for Precision Medicine - The findings suggest that the LifeClock model can predict disease risk more accurately than using chronological age alone, potentially transforming the understanding of aging and its relationship with diseases [21]. - This technology is practical and accessible, as it relies on routine clinical data rather than expensive specialized tests, making it easier to implement in existing healthcare systems [23].