Large language models (LLMs) have transformed technology beyond natural language processing, offering capabilities from general tasks to specialized areas like radiology. These models, trained on vast datasets, can perform tasks without additional fine-tuning. They are evolving rapidly, addressing challenges like hallucination, high training costs, and efficiency, while incorporating multimodal inputs. This review provides conceptual knowledge and actionable guidance for radiologists interested in using LLMs, covering their development, applications in radiology, and future directions. LLMs, such as BERT and GPT, have advanced significantly, with BERT focusing on bidirectional context understanding and GPT on generative tasks. BERT is used in biomedical and radiology fields, while GPT is suitable for chatbots. Chatbots like ChatGPT and Bing Chat have shown potential in generating radiology reports, simplifying reports, and improving communication with patients. However, they face challenges like hallucination and privacy issues. ChatGPT has demonstrated capability in radiology, providing accurate responses without additional training. It can assist in generating reports, extracting information, and simplifying complex medical jargon. However, its performance is limited by the data it was trained on, and it may not always provide accurate or up-to-date information. LLMs are also being used in research and clinical settings for tasks like structured reporting, radiology protocol suggestions, and decision-making support. However, their use requires careful consideration of data privacy and the potential for generating false information. Future directions include multimodal AI, which integrates visual and textual data, and the development of more specialized models for radiology. These advancements could significantly enhance the capabilities of LLMs in healthcare, improving diagnostic accuracy and patient care. Radiologists should be aware of the strengths and limitations of LLMs and consider their use in clinical practice with caution.Large language models (LLMs) have transformed technology beyond natural language processing, offering capabilities from general tasks to specialized areas like radiology. These models, trained on vast datasets, can perform tasks without additional fine-tuning. They are evolving rapidly, addressing challenges like hallucination, high training costs, and efficiency, while incorporating multimodal inputs. This review provides conceptual knowledge and actionable guidance for radiologists interested in using LLMs, covering their development, applications in radiology, and future directions. LLMs, such as BERT and GPT, have advanced significantly, with BERT focusing on bidirectional context understanding and GPT on generative tasks. BERT is used in biomedical and radiology fields, while GPT is suitable for chatbots. Chatbots like ChatGPT and Bing Chat have shown potential in generating radiology reports, simplifying reports, and improving communication with patients. However, they face challenges like hallucination and privacy issues. ChatGPT has demonstrated capability in radiology, providing accurate responses without additional training. It can assist in generating reports, extracting information, and simplifying complex medical jargon. However, its performance is limited by the data it was trained on, and it may not always provide accurate or up-to-date information. LLMs are also being used in research and clinical settings for tasks like structured reporting, radiology protocol suggestions, and decision-making support. However, their use requires careful consideration of data privacy and the potential for generating false information. Future directions include multimodal AI, which integrates visual and textual data, and the development of more specialized models for radiology. These advancements could significantly enhance the capabilities of LLMs in healthcare, improving diagnostic accuracy and patient care. Radiologists should be aware of the strengths and limitations of LLMs and consider their use in clinical practice with caution.