A new AI model can forecast a person’s risk of diseases across their life

Subscribe to enjoy similar stories. MUCH OF THE art of medicine involves working out, through detailed questioning and physical examination, which disease a given patient has contracted. Far harder, but no less desirable, would be identifying which diseases a patient might develop in the future.

This is what the team behind a new artificial-intelligence (AI) model, details of which were published in Nature on September 17th, claims to do. Though the model, named Delphi-2M, is not yet ready for deployment in hospitals, its creators hope it could one day allow doctors to predict if their patients are likely to get one of more than 1,000 different conditions, including Alzheimer’s disease, cancer and heart attacks, which all affect many millions every year. In addition to helping flag patients who are at high risk, it might also help health authorities allocate budgets for disease areas that may need extra funds in the future.

The model was developed by teams at the European Molecular Biology Laboratory (EMBL) in Cambridge and the German Cancer Research Centre in Heidelberg. It takes inspiration from large language models (LLMs)—such as GPT-5, which powers ChatGPT—that are capable of producing fluent prose. LLMs are trained to spot patterns in enormous amounts of text scraped from the internet, which allows them to select the word most likely to come next in any given sentence.

Delphi-2M’s creators reasoned that an AI model fed on large amounts of human-health data could have similar predictive power. In many respects, the design of established LLMs was well-suited to the task. One major tweak that was needed, however, was to teach such a model to account for the time that had passed between events in a patient’s life.