biostudies-literatureUnknown647(8088)Shmatko ADecision-making in healthcare relies on understanding patients' past and current health states to predict and, ultimately, change their future course^1-3. Artificial intelligence (AI) methods promise to aid this task by learning patterns of disease progression from large corpora of health records^4,5. However, their potential has not been fully investigated at scale. Here we modify the GPT⁶ (generative pretrained transformer) architecture to model the progression and competing nature of human diseases. We train this model, Delphi-2M, on data from 0.4 million UK Biobank participants and validate it using external data from 1.9 million Danish individuals with no change in parameters. Delphi-2M predicts the rates of more than 1,000 diseases, conditional on each Nature248-256https://www.ebi.ac.uk/biostudies/studies/S-EPMC12589094biostudies-literatureLearning the natural history of human disease with generative transformers.PMC12589094Brunak SGerstung MJung AWFitzgerald TShmatko AGaurav KBirney EMortensen LHfalseLearning the natural history of human disease with generative transformers.Decision-making in healthcare relies on understanding patients' past and current health states to predict and, ultimately, change their future course^1-3. Artificial intelligence (AI) methods promise to aid this task by learning patterns of disease progression from large corpora of health records^4,5. However, their potential has not been fully investigated at scale. Here we modify the GPT⁶ (generative pretrained transformer) architecture to model the progression and competing nature of human diseases. We train this model, Delphi-2M, on data from 0.4 million UK Biobank participants and validate it using external data from 1.9 million Danish individuals with no change in parameters. Delphi-2M predicts the rates of more than 1,000 diseases, conditional on each 2025-01-01T00:00:00Z2025 Nov2026-06-06T13:58:26.932Z2026-05-31T03:10:17.073ZS-EPMC125890944096301910.1038/s41586-025-09529-3