查看更多>>摘要：ChatGPT explores a strategic blueprint of question answering(QA)to deliver medical diagnoses,treat-ment recommendations,and other healthcare support.This is achieved through the increasing incorpo-ration of medical domain data via natural language processing(NLP)and multimodal paradigms.By transitioning the distribution of text,images,videos,and other modalities from the general domain to the medical domain,these techniques have accelerated the progress of medical domain question an-swering(MDQA).They bridge the gap between human natural language and sophisticated medical do-main knowledge or expert-provided manual annotations,handling large-scale,diverse,unbalanced,or even unlabeled data analysis scenarios in medical contexts.Central to our focus is the utilization of lan-guage models and multimodal paradigms for medical question answering,aiming to guide the research community in selecting appropriate mechanisms for their specific medical research requirements.Specialized tasks such as unimodal-related question answering,reading comprehension,reasoning,di-agnosis,relation extraction,probability modeling,and others,as well as multimodal-related tasks like vision question answering,image captioning,cross-modal retrieval,report summarization,and genera-tion,are discussed in detail.Each section delves into the intricate specifics of the respective method under consideration.This paper highlights the structures and advancements of medical domain explo-rations against general domain methods,emphasizing their applications across different tasks and datasets.It also outlines current challenges and opportunities for future medical domain research,paving the way for continued innovation and application in this rapidly evolving field.This comprehen-sive review serves not only as an academic resource but also delineates the course for future probes and utilization in the field of medical question answering.
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