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We developed the Digital Twin\u2014Generative Pretrained Transformer (DT-GPT), extending LLM-based forecasting solutions to clinical trajectory prediction. DT-GPT leverages electronic health records without requiring data imputation or normalization and overcomes real-world data challenges such as missingness, noise, and limited sample sizes. Benchmarking on non-small cell lung cancer, intensive care unit, and Alzheimer\u2019s disease datasets, DT-GPT outperformed state-of-the-art machine learning models, reducing the scaled mean absolute error by 3.4%, 1.3% and 1.8%, respectively. It maintained distributions and cross-correlations of clinical variables, and demonstrated explainability through a human-interpretable interface. Additionally, DT-GPT\u2019s ability to perform zero-shot forecasting highlights potential advantages of LLMs as clinical forecasting platforms, proposing a path towards digital twin applications in clinical trials, treatment selection, and adverse event mitigation.<\/jats:p>","DOI":"10.1038\/s41746-025-02004-3","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T10:52:55Z","timestamp":1759315975000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Large language models forecast patient health trajectories enabling digital twins"],"prefix":"10.1038","volume":"8","author":[{"given":"Nikita","family":"Makarov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Bordukova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Papichaya","family":"Quengdaeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Garger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raul","family":"Rodriguez-Esteban","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabian","family":"Schmich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael P.","family":"Menden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"2004_CR1","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1038\/nrd2246","volume":"6","author":"AD Schachter","year":"2007","unstructured":"Schachter, A. 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Hoffmann-La Roche. M.P.M. collaborates and is financially supported by GSK, F. Hoffmann-La Roche, and AstraZeneca. M.P.M. is supported by the European Union\u2019s Horizon 2020 Research and Innovation Programme (Grant agreement No. 950293\u2014COMBAT-RES). N.M., M.B., R.R.E., F.S. and M.P.M. are authors of an in-force patent entitled \u201cForecasting of subject-related attributes using generative machine-learning model\u201d (patent publication number 2025\/021719, patent application number EP2024070632) owned by F. Hoffmann-La Roche and Helmholtz Zentrum Munich. The patent covers application of large language models such as DT-GPT for forecasting of clinical trajectories of patients during a clinical trial. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"588"}}