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In this paper, we aimed to provide a review and tutorial for researchers inthe field of medical imaging using language models to improve their tasks athand. We began by providing an overview of the history and concepts of languagemodels, with a special focus on large language models. We then reviewed thecurrent literature on how language models are being used to improve medicalimaging, emphasizing different applications such as image captioning, reportgeneration, report classification, finding extraction, visual questionanswering, interpretable diagnosis, and more for various modalities and organs.The ChatGPT was specially highlighted for researchers to explore more potentialapplications. We covered the potential benefits of accurate and efficientlanguage models for medical imaging analysis, including improving clinicalworkflow efficiency, reducing diagnostic errors, and assisting healthcareprofessionals in providing timely and accurate diagnoses. Overall, our goal wasto bridge the gap between language models and medical imaging and inspire newideas and innovations in this exciting area of research. We hope that thisreview paper will serve as a useful resource for researchers in this field andencourage further exploration of the possibilities of language models inmedical imaging.

Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT