1. A multimodal generative AI copilot for human pathology.
- Author
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Lu MY, Chen B, Williamson DFK, Chen RJ, Zhao M, Chow AK, Ikemura K, Kim A, Pouli D, Patel A, Soliman A, Chen C, Ding T, Wang JJ, Gerber G, Liang I, Le LP, Parwani AV, Weishaupt LL, and Mahmood F
- Subjects
- Humans, Natural Language Processing, Male, Female, Artificial Intelligence, Clinical Decision-Making methods, Diagnostic Imaging methods, Diagnostic Imaging trends, Pathology education, Pathology methods, Pathology trends
- Abstract
Computational pathology
1,2 has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders3,4 . 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 multimodal vision-language AI assistants and GPT-4V, which powers the commercially available multimodal general-purpose AI assistant ChatGPT-4 (ref.6 ). PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases with diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive vision-language AI copilot that can flexibly handle both visual and natural language inputs, PathChat may potentially find impactful applications in pathology education, research and human-in-the-loop clinical decision-making., (© 2024. The Author(s).)- Published
- 2024
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