Fig. 6: HistoGPT produces clinically accurate and consistent pathology reports for common diseases, as confirmed in a real-world, multi-center clinical study.

a Skin biopsies were randomly collected from routine cases at the Mayo Clinic (USA), University Hospital Münster (Germany), and Radboud University Medical Center (The Netherlands). b Two board-certified (dermato-)pathologists at each site evaluated the generated reports according to the following criteria: (5) beyond expectation, (4) highly accurate, (3) generally accurate with minor variations without clinical impact, (2) partially accurate with variations that could have clinical impact, (1) minimally accurate, (0) completely inaccurate. A score greater than 2 indicates a diagnosis that is considered correct or within an acceptable range of subjectivity. c HistoGPT produced consistent and accurate reports for the most common neoplastic epithelial lesions (achieving an average score of 2 or higher for each class) and struggled with classes with little training data (<200 data points) or classes that cannot be predicted from imaging alone (re-excisions). While our training dataset (the Munich cohort) covers all diseases provided by the three institutions, it contains many heterogeneous categories with different numbers of samples and subtypes. For instance, neoplastic cases have 1554 samples across 64 diseases, with an average of 27 data points per class. In addition, reporting standards vary widely between institutions, resulting in large variability in scores. Therefore, we also examined interobserver variability by having dermatopathologists from Mayo (κ = 0.055) and Münster (κ = 0.295) review the reports generated for the Radboud cohort. Source data are provided as a Source Data file.