In 2026, we will see the beginning of the accuracy of medical forecasting. As there have been remarkable advances in weather forecasting through the use of large-scale linguistic models, so too will determining a person’s risk for major age-related diseases (cancer, cardiovascular, and neurodegenerative). These diseases share common threads, such as a long incubation period before any symptoms appear, often two decades or more. They also have the same biological basis of immunosenescence and inflammation, terms that indicate the immune system that has lost its function and protective capacity, and the inflammation that accompanies this.
The science of aging has given us new ways to track these processes with whole body and organ clocks, as well as specific protein biomarkers. That enables us to determine whether a person or a certain organ in a person is aging rapidly. Along with that, new AI algorithms can see things that medical professionals can’t, such as accurately interpreting medical images like retinal scans to predict cardiovascular diseases years in advance.
These additional layers of data can be integrated with a person’s electronic medical records, which include their formal and informal notes, lab results, scans, genetic results, wearable sensors, and environmental data. Overall, this provides an unprecedented depth of information about a person’s health status, allowing for the prediction of risk for three major diseases. Unlike a polygenic risk score that cannot predict a person’s risk of heart disease, common cancers and Alzheimer’s, precision medicine prediction takes it to a new level by providing a hypothetical temporal arc—the “culprit” factor. If all the data is analyzed with big thinking models, it can provide a person’s risk, and an individual, aggressive prevention plan.
We already know that the risk of these three diseases can be reduced with lifestyle factors, such as an anti-inflammatory diet, regular exercise, and regular, high-quality sleep patterns. But, along with paying attention to these factors, which are more likely to be used when a person is aware of their dangers, we will have medicines that will promote a healthy, protective immune system and reduce inflammation throughout the body and brain. Already GLP-1 drugs have been shown to be the best at achieving these goals, but many more drugs are still on the way.
The predictive power of precision medicine must be demonstrated and validated through prospective clinical trials that demonstrate, using the same aging metrics, that an individual’s risk is reduced. An example of people with an increased risk of Alzheimer’s is a blood test known as ip-tau217, and that risk can be significantly reduced with lifestyle improvements, especially exercise. That can be verified with brain organ clocks and whole body aging clocks.
This is a new frontier in medicine – the possibility of primary prevention of three major age-related diseases that threaten our life expectancy and quality of life. It won’t happen without advances in both aging science and AI. For me, this is the future use of AI in medicine that is very interesting: an unparalleled opportunity to prevent major diseases from happening, something that has been dreamed of but never happened at scale due to a lack of data and analysis. In 2026, it will be.
