A multimodal generative AI copilot for human pathology.

ABSTRACT: Computational pathology^1,2 has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders^3,4. However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots⁵ tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We built PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and fine-tuning the whole system on over 456,000 diverse visual-language instructions consisting of 999,202 question and answer turns. We compare PathChat with several multimo

SUBMITTER: Lu MY 

PROVIDER: S-EPMC11464372 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature