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Mingzhe Hu¹, Joshua Qian², Shaoyan Pan¹

¹Department of Computer Science and Informatics, Emory University, Atlanta, GA, United States of America.

Physics in medicine and biology

|March 27, 2024

概括

本综述探讨了医学成像研究中的大型语言模型 (LLM). 法律学增强图像分析,改善临床工作流程,减少诊断错误,推动该领域的创新.

科学领域:

人工智能的人工智能
医学成像分析医学成像分析
自然语言处理自然语言处理.

背景情况:

语言模型 (LMs) 已经显著发展,大型语言模型 (LLMs) 在各种研究领域显示出前景.
医学成像产生大量的数据,需要先进的分析工具.

研究的目的:

为在医学成像研究中实施语言模型提供综合指南.
审查医疗成像中LLM的当前应用和未来潜力.

主要方法:

对医学成像中的语言模型现有研究的文献综述.
详细检查LLM能力,包括具体的例子,如ChatGPT.
讨论LLMs对临床工作流程和诊断准确性的影响.

主要成果:

在医学成像中,LLM用于图像标题,报告生成和分类等任务.
应用包括发现提取,视觉问题答案和可解释的诊断.
法律法规提供了显著的优势,包括提高临床效率和减少诊断错误.

结论:

有效地将LLM与医学成像相结合,可以激发新的研究想法和创新.
这篇评论是研究人员探索医学成像LLM应用的宝贵资源.
对LLM的持续调查有可能促进医学成像分析和患者护理的进步.

关键词:

贝尔特 (BERT) 公司聊天GPT 聊天GPT 聊天大型语言模型医学成像医学成像多模式学习是多模式学习.

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