AI Gets Personal with Aging

Beyond the Birthday Candle Count

Researchers are training artificial intelligence models to analyze medical data and determine a person’s true biological age, rather than relying on their chronological age alone. This approach can reveal signs of premature aging or underlying health conditions before they manifest outwardly. By looking at standard medical scans, lab tests, and other health indicators, AI can paint a more nuanced picture of how well someone is aging and potentially guide more customized medical care. The ultimate goal isn’t simply to extend lifespan—but to improve healthspan, the proportion of life spent in good health. With funding from the NIH, some projects are already demonstrating that these AI models may outperform traditional assessments in predicting health outcomes.

The Promise (and Limits) of AI-Aging Metrics

While early results are promising, researchers caution that these tools are still in development and must be studied across more diverse populations. Right now, much of the data used to train the AI comes from relatively healthy and affluent groups, raising questions about accuracy and bias when the models are applied more broadly. Still, physicians and aging experts are optimistic. If refined, AI-assisted biological age assessments could become a standard part of preventive medicine—used to spot disease earlier, tailor fitness or diet recommendations, and even flag when a treatment strategy may be too aggressive or too mild for a person’s actual health trajectory.