Large language models (LLMs) are transformer-based neural networks capable of generating human-like responses, summarizing text, extracting data, and assisting in pathology. They have significant potential in pathology practice and education but require verification with reliable sources. LLMs can assist in interpreting histopathology images and are used in education, information extraction, text classification, report generation, programming, and clinical pathology. However, they can produce hallucinations and errors, leading to automation bias and de-skilling. LLMs can also be used in multi-modal applications, such as generating descriptions of microscopical images. Despite their potential, challenges include bias, inaccuracies, and the need for manual supervision. LLMs can be integrated into clinical workflows but must be used cautiously to avoid errors. Future directions include improving model accuracy, enhancing ethical guidelines, and expanding open-source models. LLMs can enhance efficiency and accuracy in pathology but require careful implementation and validation.Large language models (LLMs) are transformer-based neural networks capable of generating human-like responses, summarizing text, extracting data, and assisting in pathology. They have significant potential in pathology practice and education but require verification with reliable sources. LLMs can assist in interpreting histopathology images and are used in education, information extraction, text classification, report generation, programming, and clinical pathology. However, they can produce hallucinations and errors, leading to automation bias and de-skilling. LLMs can also be used in multi-modal applications, such as generating descriptions of microscopical images. Despite their potential, challenges include bias, inaccuracies, and the need for manual supervision. LLMs can be integrated into clinical workflows but must be used cautiously to avoid errors. Future directions include improving model accuracy, enhancing ethical guidelines, and expanding open-source models. LLMs can enhance efficiency and accuracy in pathology but require careful implementation and validation.