This review paper provides a comprehensive overview of the application of language models in medical imaging, with a focus on the capabilities of ChatGPT. It discusses the evolution of language models, their current applications in medical imaging, and the potential benefits of integrating them into clinical workflows. The paper highlights the challenges and limitations of using language models in medical imaging, including the need for specialized terminology, data privacy concerns, and the complexity of integrating vision and language models. It also explores the potential of multimodal language models, such as GPT-4-V, in enhancing the accuracy and efficiency of medical imaging analysis. The review emphasizes the importance of accurate and efficient language models in improving clinical workflow efficiency, reducing diagnostic errors, and assisting clinicians in providing timely and accurate diagnoses. The paper concludes with a discussion of the current research landscape and future directions for the application of language models in medical imaging.This review paper provides a comprehensive overview of the application of language models in medical imaging, with a focus on the capabilities of ChatGPT. It discusses the evolution of language models, their current applications in medical imaging, and the potential benefits of integrating them into clinical workflows. The paper highlights the challenges and limitations of using language models in medical imaging, including the need for specialized terminology, data privacy concerns, and the complexity of integrating vision and language models. It also explores the potential of multimodal language models, such as GPT-4-V, in enhancing the accuracy and efficiency of medical imaging analysis. The review emphasizes the importance of accurate and efficient language models in improving clinical workflow efficiency, reducing diagnostic errors, and assisting clinicians in providing timely and accurate diagnoses. The paper concludes with a discussion of the current research landscape and future directions for the application of language models in medical imaging.