The article "Applications of Large Language Models in Pathology" by Jerome Cheng explores the potential and challenges of using large language models (LLMs) in the field of pathology. LLMs, based on the transformer architecture, can generate educational materials, summarize text, extract structured data, create reports, and assist in case sign-out. They can also aid in interpreting histopathology images when combined with vision models. While LLMs show promise, they are not infallible and require verification with reputable sources. Over-reliance on AI can lead to de-skilling and automation bias. The article highlights several use cases, including education, information extraction, text classification, report generation, programming, and clinical pathology. It discusses the importance of prompt engineering and the need for human supervision to ensure accuracy and reliability. The article concludes by emphasizing the growing capabilities of LLMs and their potential to transform pathology practice, while also addressing the challenges and limitations, such as bias, knowledge gaps, and the risk of introducing errors.The article "Applications of Large Language Models in Pathology" by Jerome Cheng explores the potential and challenges of using large language models (LLMs) in the field of pathology. LLMs, based on the transformer architecture, can generate educational materials, summarize text, extract structured data, create reports, and assist in case sign-out. They can also aid in interpreting histopathology images when combined with vision models. While LLMs show promise, they are not infallible and require verification with reputable sources. Over-reliance on AI can lead to de-skilling and automation bias. The article highlights several use cases, including education, information extraction, text classification, report generation, programming, and clinical pathology. It discusses the importance of prompt engineering and the need for human supervision to ensure accuracy and reliability. The article concludes by emphasizing the growing capabilities of LLMs and their potential to transform pathology practice, while also addressing the challenges and limitations, such as bias, knowledge gaps, and the risk of introducing errors.