ChatGPT explores a strategic blueprint of question answering (QA) in delivering medical diagnosis, treatment recommendations, and other healthcare support. This is achieved through the increasing incorporation 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 expedited the progress of medical domain question answering (MDQA). They bridge the gap between human natural language and sophisticated medical domain knowledge or expert manual annotations, handling large-scale, diverse, unbalanced, or even unlabeled data analysis scenarios in medical contexts. Central to our focus is the utilizing of language 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, diagnosis, relation extraction, probability modeling, and others, as well as multimodal-related tasks like vision question answering, image caption, cross-modal retrieval, report summarization, and generation, 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 explorations 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.

通过自然语言处理和多模态范式，ChatGPT 探索了医学诊断、治疗建议和其他医疗支持的问题回答的战略蓝图，在医学领域数据的逐渐整合下，这些技术加快了医学领域问题回答的进展，通过连接人类自然语言与医学领域知识或专家手动注释的空白，处理了医学环境下大规模、多样化、不平衡或无标签数据分析的场景，强调了在不同任务和数据集中的应用，并概述了当前的挑战和未来医学领域研究的机遇和创新。

开发生物和医学 ChatGPT：生物医学问答的完整概述