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As advances in large language models (LLMs) and multimodal techniquescontinue to mature, the development of general-purpose multimodal largelanguage models (MLLMs) has surged, offering significant applications ininterpreting natural images. However, the field of pathology has largelyremained untapped, particularly in gathering high-quality data and designingcomprehensive model frameworks. To bridge the gap in pathology MLLMs, wepresent PathAsst, a multimodal generative foundation AI assistant torevolutionize diagnostic and predictive analytics in pathology. The developmentof PathAsst involves three pivotal steps: data acquisition, CLIP modeladaptation, and the training of PathAsst's multimodal generative capabilities.Firstly, we collect over 207K high-quality pathology image-text pairs fromauthoritative sources. Leveraging the advanced power of ChatGPT, we generateover 180K instruction-following samples. Furthermore, we devise additionalinstruction-following data specifically tailored for invoking eightpathology-specific sub-models we prepared, allowing the PathAsst to effectivelycollaborate with these models, enhancing its diagnostic ability. Secondly, byleveraging the collected data, we construct PathCLIP, a pathology-dedicatedCLIP, to enhance PathAsst's capabilities in interpreting pathology images.Finally, we integrate PathCLIP with the Vicuna-13b and utilizepathology-specific instruction-tuning data to enhance the multimodal generationcapacity of PathAsst and bolster its synergistic interactions with sub-models.The experimental results of PathAsst show the potential of harnessingAI-powered generative foundation model to improve pathology diagnosis andtreatment processes.

PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology