The article "Large Language Models: A Guide for Radiologists" by Sunkyu Kim, Choon-gun Lee, and Seung-seob Kim provides an overview of large language models (LLMs) and their potential applications in radiology. LLMs, which have revolutionized technology beyond natural language processing, can handle a wide range of tasks, including those specific to radiology, without additional fine-tuning. The authors discuss the evolution of language models from early models like Bag of Words to more advanced models such as transformers, which have significantly enhanced the capabilities of NLP models. They highlight the importance of LLMs in improving radiologists' efficiency and productivity through tasks such as generating radiology reports, structured reporting, and simplifying technical jargon. The article also explores the performance of LLMs like ChatGPT in radiology-specific tasks, noting that while they can provide appropriate responses to non-expert-level questions, their performance in more specialized areas may vary. Studies have shown that ChatGPT can generate correct answers in radiology quizzes and perform well in radiology board-style examinations. However, the authors caution against the limitations of LLMs, such as "hallucination" (providing incorrect or implausible information) and privacy concerns, especially when dealing with real patient data. Finally, the article discusses the future direction of multimodal AI, which combines visual and textual inputs, and its potential applications in radiology. The authors conclude that LLMs hold significant promise for enhancing clinical workflows and future research, but radiologists should carefully consider the characteristics of specific tasks before selecting appropriate models.The article "Large Language Models: A Guide for Radiologists" by Sunkyu Kim, Choon-gun Lee, and Seung-seob Kim provides an overview of large language models (LLMs) and their potential applications in radiology. LLMs, which have revolutionized technology beyond natural language processing, can handle a wide range of tasks, including those specific to radiology, without additional fine-tuning. The authors discuss the evolution of language models from early models like Bag of Words to more advanced models such as transformers, which have significantly enhanced the capabilities of NLP models. They highlight the importance of LLMs in improving radiologists' efficiency and productivity through tasks such as generating radiology reports, structured reporting, and simplifying technical jargon. The article also explores the performance of LLMs like ChatGPT in radiology-specific tasks, noting that while they can provide appropriate responses to non-expert-level questions, their performance in more specialized areas may vary. Studies have shown that ChatGPT can generate correct answers in radiology quizzes and perform well in radiology board-style examinations. However, the authors caution against the limitations of LLMs, such as "hallucination" (providing incorrect or implausible information) and privacy concerns, especially when dealing with real patient data. Finally, the article discusses the future direction of multimodal AI, which combines visual and textual inputs, and its potential applications in radiology. The authors conclude that LLMs hold significant promise for enhancing clinical workflows and future research, but radiologists should carefully consider the characteristics of specific tasks before selecting appropriate models.