A multimodal generative AI copilot for human pathology, PathChat, has been developed to assist in pathology diagnosis and research. The model is built using a custom, fine-tuned multimodal large language model (MLLM) that combines a vision encoder with a large language model. PathChat was trained on over 456,000 diverse visual language instructions, allowing it to understand and respond to complex pathology-related queries. The model was evaluated against several multimodal vision language AI assistants and GPT4V, a commercial solution. PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions and produced more accurate and pathologist-preferable responses to open-ended questions. It demonstrated superior performance compared to other models, including GPT4V, in both diagnostic accuracy and the quality of responses. PathChat can handle both visual and natural language inputs, making it a versatile tool for pathology education, research, and clinical decision-making. The model was also tested on a benchmark of open-ended pathology questions, where it outperformed other models in accuracy and relevance. PathChat's ability to analyze histology images and provide detailed morphological descriptions, combined with its capacity to interpret results in the context of diagnostic guidelines, makes it a valuable tool for pathology. The model's performance was further validated through human expert evaluation, showing that it can effectively assist pathologists in making diagnoses and suggesting further testing. The study highlights the potential of multimodal generative AI in pathology, emphasizing the importance of natural language and human interaction in AI model design and user experience. The results suggest that PathChat can serve as a valuable tool for pathology education, research, and clinical decision-making.A multimodal generative AI copilot for human pathology, PathChat, has been developed to assist in pathology diagnosis and research. The model is built using a custom, fine-tuned multimodal large language model (MLLM) that combines a vision encoder with a large language model. PathChat was trained on over 456,000 diverse visual language instructions, allowing it to understand and respond to complex pathology-related queries. The model was evaluated against several multimodal vision language AI assistants and GPT4V, a commercial solution. PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions and produced more accurate and pathologist-preferable responses to open-ended questions. It demonstrated superior performance compared to other models, including GPT4V, in both diagnostic accuracy and the quality of responses. PathChat can handle both visual and natural language inputs, making it a versatile tool for pathology education, research, and clinical decision-making. The model was also tested on a benchmark of open-ended pathology questions, where it outperformed other models in accuracy and relevance. PathChat's ability to analyze histology images and provide detailed morphological descriptions, combined with its capacity to interpret results in the context of diagnostic guidelines, makes it a valuable tool for pathology. The model's performance was further validated through human expert evaluation, showing that it can effectively assist pathologists in making diagnoses and suggesting further testing. The study highlights the potential of multimodal generative AI in pathology, emphasizing the importance of natural language and human interaction in AI model design and user experience. The results suggest that PathChat can serve as a valuable tool for pathology education, research, and clinical decision-making.