Researchers in Europe have unveiled a new artificial intelligence model that can forecast a person’s risk of developing over 1,000 diseases more than a decade in advance.

The generative AI tool, called Delphi-2M after the ancient Greek oracle, was custom-built by scientists from the European Molecular Biology Laboratory, the German Cancer Research Centre and the University of Copenhagen.

Outlined in a Nature paper Wednesday, the new model was trained solely on a dataset from the United Kingdom, which included nearly a half-million individuals, and was validated using data from about 2 million participants in Denmark. It functions by analyzing a person’s history of “medical events” to evaluate the likelihood of developing specific illnesses.

“Medical events often follow predictable patterns,” said Tomas Fitzgerald, a staff scientist at EMBL’s European Bioinformatics Institute. “Our AI model learns those patterns and can forecast future health outcomes.”

A wide range of prediction algorithms and AI models already exist for specific diseases, for example cardiovascular disease and cancer. They use broad population data that includes genetics, lifestyle, socioeconomic status, region and more to predict the probability that a particular illness will arise in a person or a population.

Researchers have noted that very few models can predict a broad spectrum of human illnesses. To address this challenge, Delphi-2M was developed.

Predicting disease progression is fundamental for preventative healthcare and serves as an anchor for serving vulnerable populations and patients, especially those who are aging. For example, the probability of diseases of the heart and cancer increases as people age. The “Health inequalities 2040” report from The Health Foundation, in the U.K., projected that the number of working-age people with major illnesses such as depression, asthma, heart disease and dementia will increase from 3 million to 3.7 million by 2040, concentrated in more deprived areas.

The researchers modified a type of large language model called a generative pretrained transformer, the same kind that underpins the models running well-known chatbots such as OpenAI’s ChatGPT. Transformers ingest vast amounts of data to generate predictive outputs based on conditions presented to them. In the case of a chatbot, that would be a question or a statement; for Delphi-2M, that’s the past medical history of an individual.

According to the researchers, the model also incorporates age, sex, body mass index and health-related habits, such as tobacco use and alcohol consumption. The key distinguishing feature between Delphi and basic GPT models is that the AI algorithm can calculate absolute rates, providing consistent estimates.

From testing, Delphi-2Ms’ predictions matched or exceeded the accuracy of current models for most diseases. It also proved to be superior to the Qrisk method, a prediction algorithm used to calculate the risk of having a heart attack or stroke over the next decade.

The model works best on diseases that follow a predictable pattern of progression, such as certain types of cancer. It works by calculating the probability of a person developing a particular illness for up to 20 years.

Image: SiliconANGLE/Microsoft Designer