Researchers at the German Cancer Research Center have developed an artificial intelligence model, Delphi-2M, that can predict an individual’s risk for more than 1,000 diseases up to two decades into the future using medical records.

This development aligns with a broader shift in healthcare from reactive treatment to proactive prevention. While algorithms have been created to predict the risk of single conditions, diseases are often interconnected. A comprehensive model that can account for this complexity could inform early treatment, improve targeted screening, and identify high-risk individuals who might otherwise be overlooked.

How Delphi-2M works

The Delphi-2M model is a large language model (LLM), similar to the technology behind text-generating chatbots. Instead of being trained on internet text, it was developed by processing over 400,000 comprehensive medical records from the UK Biobank. This clinical data was supplemented with lifestyle information, such as body mass index and smoking status.

The model treats a patient’s medical history as a sequence of “disease tokens,” where each diagnostic code represents a step in a potential disease progression. By analyzing these sequences, the AI learns the statistical patterns of how different conditions connect and follow one another over time. A key feature is its ability to dynamically re-evaluate predictions. When new information, like a recent blood test result, is added, the model can update its risk calculations for that individual, allowing for continuous health monitoring.

Performance and validation

In performance evaluations, Delphi-2M matched or exceeded the accuracy of established clinical risk scores for the majority of the 1,258 diseases it was trained on. It also outperformed other specialized medical AI predictors designed to forecast single diseases. The model proved particularly effective in predicting the long-range risk of cardiovascular disease and dementia, showing greater accuracy than some biomarker-based models even when forecasting two decades into the future.

However, the model struggled to accurately predict conditions with more variable trajectories heavily influenced by lifestyle changes, such as Type 2 diabetes. This indicates a limitation in its ability to account for factors not consistently captured in electronic health records.

To test its robustness, the researchers applied the model to the Danish National Patient Registry, which contains records for nearly two million citizens. Despite differences in the populations and healthcare systems, the model’s prediction accuracy remained high, suggesting it learned fundamental principles of human disease progression.

Ethical design and future applications

Delphi-2M was designed with practical and ethical considerations in mind. It can learn from synthetic medical records to protect patient privacy and is an “explainable” AI, meaning it can provide a rationale for its predictions by clustering related conditions and symptoms. The researchers emphasize that the model identifies statistical associations, not causation.

The model is built with a modular design to incorporate additional data types in the future, such as genomics, diagnostic imaging, and data from wearable devices. Currently, the tool is being tested in other countries with diverse populations. In its present form, it could be used in clinical settings to identify individuals who would benefit from early screening, even if they do not meet traditional criteria.

Expert reception

The model has been positively received by experts not involved in the study. Justin Stebbing, a professor at Anglia Ruskin University, called the tool “an achievement” that sets “a new standard for both predictive accuracy and interpretability.” Gustavo Sudre, a researcher at King’s College London, described the research as:

“a significant step towards scalable, interpretable, and—most importantly—ethically responsible form of predictive modeling in medicine.”

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