Data is the key to slowing age-related diseases

In 2026, we will see the start of precision medical forecasting. Just as remarkable progress has been made in weather forecasting through the use of large linguistic models, so will it be in determining an individual’s risk of major age-related diseases (cancer, cardiovascular and neurodegenerative). These diseases share commonalities, such as a long incubation phase before symptoms appear, usually two decades or more. They also have the same biological underpinnings of immunosenescence and inflammation, terms that characterize an immune system that has lost some of its functionality and protective power, and the increased inflammation that accompanies it.

The science of aging has given us new ways to track these processes through body and organ clocks, as well as specific protein biomarkers. This allows us to determine whether a person or human organ is aging at an accelerated rate. Along with this, 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 years in advance.

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

We already know that the risk of these three diseases can be reduced through 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 much more likely to be implemented when an individual is aware of their risk, we will have medications that will promote a healthy, protective immune system and reduce inflammation throughout the body and brain. GLP-1 drugs have already proven to be pioneers in achieving these goals, but many more drugs are in the pipeline.

The potential for accurate medical prediction must be demonstrated and validated by prospective clinical trials that show, using the same aging parameters, that a person’s risk is reduced. An example for people at increased risk of Alzheimer’s disease is the blood test known as p-tau217and this risk can be significantly reduced through improvement in lifestyle factors, particularly exercise. This could be confirmed by a brain organ clock and body-wide aging clocks.

This is a new frontier in medicine: the potential for primary prevention of the three major age-related diseases that compromise our health and quality of life. This would not be possible without advances in the science of aging and AI. To me, this is the most exciting future use of AI in medicine: an unprecedented opportunity to prevent the onset of major diseases, something that has been dreamed of but has not been achievable at scale due to lack of data and analysis. In 2026, this will finally be the case.