清华大学开发AI大模型，准确预测人类衰老，登上医学顶刊Nature Medicine

Core Viewpoint - The article discusses a groundbreaking research study that introduces a large language model (LLM)-based biological age prediction method, which estimates overall and organ-specific aging through health examination reports, aiming to enhance health management for the general public [3][4][5]. Group 1: Research Background and Importance - Accurately assessing an individual's aging level is crucial for identifying health risks and preventing age-related diseases, yet current aging indicators face methodological limitations and lack broad applicability [2][8]. - Aging is a major risk factor for mortality and chronic diseases, contributing significantly to societal health burdens, and understanding both overall and organ-specific aging is essential for comprehensive health assessments [7]. Group 2: Methodology and Framework - The research team developed a novel framework that converts health examination data (e.g., blood pressure, liver function) into textual reports for input into a large language model (e.g., Llama3), which analyzes numerous indicators to produce two key outputs: overall biological age and organ-specific ages for six major organs [10][11]. - The LLM does not rely on preset formulas but utilizes a pre-trained medical knowledge base to intelligently infer aging metrics based on individual health details [12]. Group 3: Validation and Results - The study validated its predictive framework using data from over 10 million individuals across six major databases, achieving impressive accuracy rates: 75.7% for predicting all-cause mortality risk, 70.9% for coronary heart disease risk, and 81.2% for liver cirrhosis risk, outperforming traditional models [15][20]. - The age difference predicted by the LLM correlates with increased health risks, with each additional year in predicted age raising all-cause mortality risk by 5.5% and coronary heart disease risk by 7.2% [16]. Group 4: Clinical Applications and Innovations - The research introduces a disease radar warning system, revealing that an increase in cardiovascular age difference correlates with a 45% increase in coronary heart disease risk, while liver age difference correlates with a 63% increase in liver cirrhosis risk [19]. - The study identifies 322 key proteins as potential "aging accelerators," with 56.7% being new targets linked to mortality risk, highlighting the predictive power of the LLM in personalized health management [19]. - By analyzing three years of continuous health examination data, the LLM can generate individual aging rate curves, improving disease outbreak predictions by three times compared to single examination assessments [19].