Consistent performance of large language models in rare disease diagnosis across ten languages and 4917 cases.

ABSTRACT:

Background

Large language models (LLMs) are increasingly used medicine for diverse applications including differential diagnostic support. The training data used to create LLMs such as the Generative Pretrained Transformer (GPT) predominantly consist of English-language texts, but LLMs could be used across the globe to support diagnostics if language barriers could be overcome. Initial pilot studies on the utility of LLMs for differential diagnosis in languages other than English have shown promise, but a large-scale assessment on the relative performance of these models in a variety of European and non-European languages on a comprehensive corpus of challenging rare-disease cases is lacking.

Methods

We created 4917 clinical vignettes using structured data captured with Huma

SUBMITTER: Chimirri L 

PROVIDER: S-EPMC12552141 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature