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评估用于自动报告和数据系统的大型语言模型分类:跨部门研究

Qingxia Wu¹, Qingxia Wu^2,3, Huali Li⁴

¹Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, China.

JMIR medical informatics

|July 17, 2024

概括

大型语言模型对放射学具有前景,但它们在报告和数据系统 (RADS) 分类方面的表现各不相同. 采用结构化提示和指南PDF文件的Claude-2实现了更高的准确性,特别是在LI-RADS 2018中.

科学领域:

医疗成像中的人工智能
在医疗保健中的自然语言处理.
放射学工作流的优化工作流的优化

背景情况:

大型语言模型 (LLM) 提供了增强放射学工作流程的潜力.
在结构化放射学任务上LLM的性能,如报告和数据系统 (RADS) 分类,在很大程度上是未经探索的.
本研究探讨了标准化放射学报告中的LLM能力.

研究的目的:

为了评估三个LLM聊天机器人:Claude-2,GPT-3.5和GPT-4.
评估他们对放射学报告分配RADS类别的准确性.
确定不同提示策略对LLM绩效的影响.

主要方法:

一项横截面研究比较了三个聊天机器人,使用Li-RADS,肺-RADS和O-RADS的30份放射学报告.
采用了三级提示策略:零射击,少数射击和指南PDF信息提示.
放射学报告是由经过董事会认证的放射科医生准备的,聊天机器人的反应由盲目审查员评估.

主要成果:

克劳德-2显示了最高的准确性 (平均57%),使用几次射击提示和指导PDF文件,特别是LI-RADS 2018 (75%准确性).
提示工程显著提高了所有聊天机器人的准确性;克劳德-2以特定提示显示了增强的性能,与GPT-4不同.
与Lung-RADS 2022和O-RADS相比,聊天机器人在LI-RADS 2018中表现更好.

结论:

克劳德-2显示了RADS分类的潜力,当提供结构化的提示和指南PDF时,特别是LI-RADS 2018.
目前的LLM一代在根据最近的RADS标准准确地分类案件方面扎.
需要进一步开发,以提高在各种放射性报告场景中LLM的准确性和可靠性.

关键词:

聊天GPT 聊天在GPT 聊天李-拉德斯 (LI-RADS) 公司肺-RADS 肺-RADS 的使用情况.在O-RADS中使用O-RADS.放射学报告和数据系统准确度准确度准确度准确度准确度分类分类的分类.聊天机器人聊天机器人聊天机器人聊天机器人聊天机器人大型语言模型推是指一个建议.建议建议建议建议建议建议

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Classification of Systems-I

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Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,