Data Holds the Key in Slowing Age-Related Illnesses

In 2026, we will see the beginning of precision medical forecasting. Just as there have been remarkable advances in weather forecasting with the use of large language models, so will there be for determining an individual’s risk of the major age-related diseases (cancer, cardiovascular, and neurodegenerative). These diseases share common threads, such as a long incubation phase before any symptoms are manifest, usually two decades or more. They also have the same biologic underpinnings of immunosenescence and inflammaging, terms that characterize an immune system that has lost some of its functionality and protective power, and the accompanying heightened inflammation.

The science of aging has given us new ways to track these processes with body-wide and organ clocks, along with specific protein biomarkers. That enables us to determine whether a person or an organ within a person is aging at an accelerated pace. Along with that, new AI algorithms can see things that medical experts cannot, such as accurately interpreting medical images like retinal scans to predict cardiovascular and neurodegenerative diseases many years in advance.

These added layers of data can be combined with a person’s electronic medical records, which include their structured and unstructured notes, lab results, scans, genetic results, wearable sensors, and environmental data. In aggregate, this provides an unprecedented depth of information about the person’s health status, enabling a forecast for risk of the three major diseases. Unlike a polygenic risk score which can detect a person’s risk for heart disease, the common cancers and Alzheimer’s, precision medical forecasting takes it to a new level by providing the projected temporal arc—the “when” factor. When all of the data is analyzed with large reasoning models, it can provide a person’s vulnerabilities, and an individualized, aggressive preventive program.

We already know the risk of these three diseases can be reduced with lifestyle factors, such as an optimal anti-inflammatory diet, frequent exercise, and a regular, high-quality sleep pattern. But, along with attention to these factors, which are far more likely to be implemented when an individual is cognizant of their risk, we will have medications that will promote a healthy, protective immune system and reduce body-wide and brain inflammation. Already the GLP-1 medicines have been shown to be a front-runner for achieving these goals, but many more medications are in the pipeline.

The potential for precision medical forecasting has to be demonstrated and validated via prospective clinical trials that show, using the same metrics of aging, that a person’s risk is decreased. An example for people with increased risk of Alzheimer’s is the blood test known as p-tau217, and that risk can be markedly reduced with improved lifestyle factors, especially exercise. That could be confirmed with a brain organ clock and body-wide aging clocks.

This is a new frontier in medicine—the potential for primary prevention of the three age-related major diseases that compromise our health span and quality of life. It would not be possible without the advances in both the science of aging and AI. For me, this is the most exciting future use of AI in medicine: an unparalleled opportunity to prevent the major diseases from occurring, something that has been dreamed about but has not been possible at scale due to the deficiency in data and analytics. In 2026, it finally will.