Data Holds the Key in Slowing Age-Related Illnesses

In 2026, we Will see the beginning of accurate medical prognosis. Just as there has been significant progress in predicting weather using large language models, there will also be significant progress in determining an individual’s risk of major age-related diseases (cancer, cardiovascular and neurodegenerative). These diseases have common threads, such as a long incubation phase, usually two decades or more, before any symptoms appear. They also have similar biological bases to immunosenescence and inflammation, terms that characterize an immune system that has lost some of its functionality and protective power, accompanied by increased inflammation.

The science of aging has given us new ways to track these processes with specific protein biomarkers as well as body-wide and organ clocks. This enables us to determine if a person or any organ within him or her is aging at an accelerated rate. Additionally, new AI algorithms can see things that medical experts cannot, such as accurately interpreting medical images such as retina scans to predict cardiovascular and neurodegenerative diseases many years in advance.

These additional layers of data can be combined with a person’s electronic medical record, including their structured and unstructured notes, lab results, scans, genetic results, wearable sensors, and environmental data. Overall, it provides unprecedented depth of information about a person’s health status, allowing them to predict the risk of three major diseases. Unlike polygenic risk scores, which can determine a person’s risk of heart disease, common cancers, and Alzheimer’s, precision medicine prediction takes this to a new level by providing an estimated temporal arc – the “when” factor. When all the data is analyzed with big logic models, it can pinpoint an individual’s vulnerabilities and provide a personalized, aggressive preventive program.

We already know that the risk of these three diseases can be reduced by lifestyle factors, such as an optimal anti-inflammatory diet, frequent exercise, and regular, high-quality sleep. But, along with attention to these factors, which are more likely to be implemented when a person knows their risk, we will have drugs that will promote a healthy, protective immune system and reduce inflammation throughout the body and brain. GLP-1 drugs are already considered leaders in achieving these goals, but many more drugs are in the pipeline.

The ability to make accurate medical predictions must be demonstrated and validated through prospective clinical trials, using metrics similar to aging that show how an individual’s risk decreases. One example is a blood test for people at increased risk of Alzheimer’s known as p-tau217, and that risk can be reduced with improved lifestyle factors, especially exercise. This can be confirmed by the brain organ clock and body-wide aging clocks.

This is a new frontier in medicine – the ability to provide primary prevention of three major age-related diseases that compromise our health span and quality of life. This would not be possible without advances in both the science of aging and AI. To me, this is the most exciting future use of AI in medicine: a unique opportunity to prevent major diseases from occurring, something that has been dreamed about but has not been possible on a large scale due to a lack of data and analytics. In 2026, it will finally happen.