任务(项目管理)
生成语法
计算机科学
人工智能
自然语言处理
管理
经济
作者
Ming Y. Lu,Bowen Chen,Drew F. K. Williamson,Richard J. Chen,Melissa Zhao,Adrian Chow,Kenji Ikemura,Ahrong Kim,Dimitra Pouli,Ankush Patel,Amr Soliman,Chengkuan Chen,Tong Ding,Judy J. Wang,Georg K. Gerber,Ivy Liang,Long P. Le,Anil V. Parwani,Luca L. Weishaupt,Faisal Mahmood
出处
期刊:Nature
[Springer Nature]
日期:2024-06-12
被引量:2
标识
DOI:10.1038/s41586-024-07618-3
摘要
The field of computational pathology[1,2] has witnessed remarkable 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 has been limited study on building general purpose, multimodal AI assistants and copilots[5] tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We build PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and finetuning the whole system on over 456,000 diverse visual language instructions consisting of 999,202 question-answer turns. We compare PathChat against several multimodal vision language AI assistants and GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4[7]. PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases of 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 and general vision-language AI Copilot that can flexibly handle both visual and natural language inputs, PathChat can potentially find impactful applications in pathology education, research, and human-in-the-loop clinical decision making.
科研通智能强力驱动
Strongly Powered by AbleSci AI