A group of researchers has unveiled an AI model that can predict a person’s likelihood of developing more than 1,000 diseases, and the AI even estimates when those illnesses might strike. Described this week in Nature, the AI is code named Delphi-2M and the tool was trained on anonymized health data from nearly 2.3 million people in the UK and Denmark. This marks one of the largest efforts yet to use generative AI to map out the future of human health.
Different than traditional health calculators that only cover specific conditions (e.g. heart disease, diabetes), Delphi-2M takes a holistic approach. This means that the AI tool actually simulates possible trajectories of your health over decades, forecasting sequences of complications, including illnesses, sleep patterns and other aspects affecting health.
How Delphi-2M works
Built on the same technology behind chatbots like ChatGPT, what makes Delphi-2M different than an LLM is that it is designed to handle medical histories not text. Each diagnosis, demographic detail or lifestyle factor is encoded like a “token,” letting the AI analyze the progression of disease in the same way a language model predicts and writes the next word.
Key inputs include:
- Age and sex
- Past diagnoses spanning 1,000+ conditions
- Lifestyle factors such as BMI, smoking and alcohol use
Utilizing this vital, yet somewhat basic information, the model then predicts both the next disease a patient might face and the length of time until that disease will appear. In tests, it reached an average accuracy score (AUC) of 0.76 across hundreds of diseases in the UK dataset; a strong result given the complexity of human health.
Results, but with caveats
As mentioned in the study, when researchers asked Delphi-2M to generate synthetic health futures for people at age 60, the projections closely matched population-level outcomes a decade later. That suggests it could become a powerful tool for public health planning such as in identifying which diseases are likely to surge among the future generations.
As with any AI, the technology isn’t perfect and there are caveats. Accuracy dropped when applied to Danish data, showing the model isn’t equally reliable across populations. Additionally, like all predictive AI, the model reflects the biases of the datasets it was trained on. For example, the UK Biobank data skews toward wealthier, healthier participants, which could distort risk estimates for underrepresented groups.
It is trustworthy?
It’s important to remember that human oversight is absolutely necessary and AI is not a replacement for a human doctor. That’s why researchers warn that Delphi-2M is not a diagnostic tool, at least not for the time being. Instead, researchers are thinking of it more as a useful forecasting engine that can detect general risks and planning preventive care. Predicting that you’re at high risk for cancer at 72 doesn’t mean it will happen, only that you resemble people who developed it in the training data.
That said, the possibilities are striking. It’s possible that more AI models like Delphi-2M could sit alongside existing health calculators, offering patients and doctors more personalized roadmaps of future risk, even surfacing actionable steps to delay or prevent illness.
The takeaway
Although it’s still research, the promise of AI-guided medicine comes with many questions. Can the same generative tech that allows ChatGPT or Claude to write code also be the same AI that predicts disease?
Delphi-2M hints at a future where your doctor might use AI to scan decades of your potential health journey, helping you take preventive action long before symptoms appear.
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