Data holds the key in slowing age-related diseases

In 2026, we are We will see the beginning of accurate medical prediction. Just as there has been significant progress in predicting the weather using large language models, there will also be significant progress in determining an individual’s vulnerability to major age-related diseases (cancer, cardiovascular disease, and neurodegenerative diseases). These diseases share common threads, such as a long incubation phase before any symptoms appear, usually lasting 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 functions and protective power, and the accompanying increased inflammation.

Gerontology has given us new ways to track these processes through body clocks and organs, along with specific protein biomarkers. This allows us to determine whether a person or an organ within a person is aging at an accelerated pace. Besides, new AI algorithms can see things that medical experts cannot, such as accurately interpreting medical images such as retinal scans to predict cardiovascular diseases and neurodegenerative diseases many years ahead.

These additional layers of data can be integrated with a person’s electronic medical records, which include their structured and unstructured notes, lab results, scans, genetic results, wearable sensors, and environmental data. Altogether, this provides an unprecedented depth of information about a person’s health status, enabling prediction of risks for the three major diseases. Unlike a polygenic risk score that can detect a person’s risk of heart disease, common cancers and Alzheimer’s disease, accurate medical prediction takes it to a new level by providing the expected time arc – the “when” factor. When all the data is analyzed using large thinking models, it can provide a person’s vulnerabilities and a strong individual prevention program.

We already know that the risk of developing these three diseases can be reduced through lifestyle factors, such as an optimal anti-inflammatory diet, frequent exercise, and a regular, high-quality sleep pattern. But alongside attention to these factors, which is more likely to be implemented when the individual is aware of their risks, we will have medications that will promote a healthy, protective immune system and reduce inflammation in the body and brain. GLP-1 drugs have already been shown to be the most likely to achieve these goals, but there are many others in the pipeline.

The possibility of accurate medical prediction must be proven and validated by future clinical trials that show, using the same measures of aging, a reduction in a person’s risk. An example of people with an increased risk of developing Alzheimer’s disease is a blood test known as p-tau217, and this risk can be significantly reduced by improving lifestyle factors, especially exercise. This can be confirmed by the brain’s organ clock and aging clocks throughout the body.

This is a new frontier in medicine – the possibility of primary prevention of the three major age-related diseases that threaten our health and quality of life. This would not be possible without advances in gerontology and artificial intelligence. To me, this is the most exciting future use of AI in medicine: an unparalleled opportunity to prevent major diseases, something that has been dreamed of but has not been widely possible due to a lack of data and analytics. In 2026, it will finally happen.