Abstract：
OBJECTIVE: This paper offers a mini-review of OpenAI\'s language model, ChatGPT, detailing its mechanisms, applications in healthcare, and comparisons with other large language models (LLMs).
METHODS: The underlying technology of ChatGPT is outlined, focusing on its neural network architecture, training process, and the role of key elements such as input embedding, encoder, decoder, attention mechanism, and output projection. The advancements in GPT-4, including its capacity for internet connection and the integration of plugins for enhanced functionality are discussed.
RESULTS: ChatGPT can generate creative, coherent, and contextually relevant sentences, making it a valuable tool in healthcare for patient engagement, medical education, and clinical decision support. Yet, like other LLMs, it has limitations, including a lack of common sense knowledge, a propensity for hallucination of facts, a restricted context window, and potential privacy concerns.
CONCLUSIONS: Despite the limitations, LLMs like ChatGPT offer transformative possibilities for healthcare. With ongoing research in model interpretability, common-sense reasoning, and handling of longer context windows, their potential is vast. It is crucial for healthcare professionals to remain informed about these technologies and consider their ethical integration into practice.
METHODS: The underlying technology of ChatGPT is outlined, focusing on its neural network architecture, training process, and the role of key elements such as input embedding, encoder, decoder, attention mechanism, and output projection. The advancements in GPT-4, including its capacity for internet connection and the integration of plugins for enhanced functionality are discussed.
RESULTS: ChatGPT can generate creative, coherent, and contextually relevant sentences, making it a valuable tool in healthcare for patient engagement, medical education, and clinical decision support. Yet, like other LLMs, it has limitations, including a lack of common sense knowledge, a propensity for hallucination of facts, a restricted context window, and potential privacy concerns.
CONCLUSIONS: Despite the limitations, LLMs like ChatGPT offer transformative possibilities for healthcare. With ongoing research in model interpretability, common-sense reasoning, and handling of longer context windows, their potential is vast. It is crucial for healthcare professionals to remain informed about these technologies and consider their ethical integration into practice.
摘要：
目的：本文对OpenAI的语言模型进行了小型回顾，ChatGPT,详细说明其机制，在医疗保健中的应用，以及与其他大型语言模型(LLM)的比较。
方法：概述了ChatGPT的基础技术，专注于它的神经网络架构，培训过程,以及输入嵌入等关键元素的作用，编码器,解码器,注意机制，和输出投影。讨论了GPT-4的进步，包括其互联网连接能力和集成插件以增强功能。
结果：ChatGPT可以产生创意，连贯,和上下文相关的句子，使其成为医疗保健中患者参与的有价值的工具，医学教育,和临床决策支持。然而，像其他法学硕士一样，它有局限性，包括缺乏常识知识，对事实产生幻觉的倾向，受限制的上下文窗口，和潜在的隐私问题。
结论：尽管存在局限性，像ChatGPT这样的LLM为医疗保健提供了变革性的可能性。随着模型可解释性研究的不断进行，常识推理，和处理较长的上下文窗口，他们的潜力是巨大的。对于医疗保健专业人员来说，了解这些技术并考虑将其道德融入实践至关重要。
方法：概述了ChatGPT的基础技术，专注于它的神经网络架构，培训过程,以及输入嵌入等关键元素的作用，编码器,解码器,注意机制，和输出投影。讨论了GPT-4的进步，包括其互联网连接能力和集成插件以增强功能。
结果：ChatGPT可以产生创意，连贯,和上下文相关的句子，使其成为医疗保健中患者参与的有价值的工具，医学教育,和临床决策支持。然而，像其他法学硕士一样，它有局限性，包括缺乏常识知识，对事实产生幻觉的倾向，受限制的上下文窗口，和潜在的隐私问题。
结论：尽管存在局限性，像ChatGPT这样的LLM为医疗保健提供了变革性的可能性。随着模型可解释性研究的不断进行，常识推理，和处理较长的上下文窗口，他们的潜力是巨大的。对于医疗保健专业人员来说，了解这些技术并考虑将其道德融入实践至关重要。
