Fig. 1: HistoGPT, a foundation vision language model for dermatopathology.

a Traditionally, pathologists analyze tissue samples from patients under a microscope and summarize their findings in a comprehensive pathology report. This manual process is time-consuming, labor-intensive, and non-standardized. b HistoGPT generates human-level written reports, provides disease classification, discriminates between tumor subtypes, predicts tumor depth, detects tumors at surgical margins, and returns text-to-image gradient-attention maps that provide model explainability. All of this serves as a second opinion for the pathologist, who can use the output of HistoGPT as a general overview and first draft for the final report. The generated reports can also be used to fill in standardized templates, as used by some institutions, by extracting the relevant keywords. c An example output for a basal cell carcinoma case from our external Münster cohort. More examples can be viewed interactively at this hyperlink. Source data are provided as a Source Data file.