Histopathology is considered the gold standard for determining the presence and nature of disease, particularly cancer. However, the process of analyzing tissue samples and producing a final pathology report is time-consuming, labor-intensive, and non-standardized. Therefore, new technological solutions are being sought to reduce the workload of pathologists. In this work, we present HistoGPT, a vision language model that takes digitized slides as input and generates reports that match the quality of human-written reports, as confirmed by natural language processing metrics and domain expert evaluations. We show that HistoGPT generalizes to five international cohorts and can predict tumor subtypes and tumor thickness in a zero-shot fashion. Our work represents an important step toward integrating AI into the medical workflow. We publish both model code and weights so that the scientific community can apply and improve HistoGPT to advance the field of computational pathology.

M.T. is employed by Roche Diagnostics GmbH but conducted his research independently of his work at Roche Diagnostics GmbH as a guest scientist at Helmholtz Munich (Helmholtz Zentrum Muenchen - Deutsches Forschungszentrum fuer Gesundheit und Umwelt GmbH).

M.T., S.W.J., and V.K. are supported by the Helmholtz Association under the joint research school "Munich School for Data Science - MUDS". C.M. acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no. 866411) and support from the Hightech Agenda Bayern.

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All research procedures were conducted in accordance with the Declaration of Helsinki. Ethics approval was granted by the Ethics Commission of the Technical University Munich (reference number 2024-98-S-CB) and the Ethics Commission of Westfalen-Lippe (reference number 2024-157-b-S).

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