A study published in Nature Medicine demonstrated an unexpected ability of large language models: predicting biological aging. The research introduced a framework that leverages LLMs to analyze diverse and unstructured data such as clinical notes or personal records to predict an individual's aging magnitude across populations.
These language model–derived predictions exhibited strong correlations with multiple conventional aging-related outcomes, indicating that LLMs could provide novel insights into age-related biology.
This discovery goes beyond the usual generative text capabilities of LLMs, highlighting their potential to support biomedical and aging research applications.
