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聊天GPT和GPT-4的潜力用于对肺癌的自由文本CT报告的数据挖掘

Matthias A Fink¹, Arved Bischoff¹, Christoph A Fink¹

¹From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., J.K., L.D., C.P.H., H.U.K., T.F.W.) and Department of Radiation Oncology (C.A.F.), University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany; Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research, Heidelberg, Germany (M.A.F., A.B., L.D., C.P.H., H.U.K., T.F.W.); and Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Heidelberg Thoracic Clinic, University of Heidelberg, Heidelberg, Germany (C.P.H.).

|September 19, 2023

概括

在从CT报告中提取肺癌表型方面,GPT-4显著优于ChatGPT,在损伤测量,转移识别和瘤进展评估方面显示出更高的准确性,错误较少.

科学领域:

医疗成像中的人工智能
对于瘤学的自然语言处理.
放射学报告分析分析

背景情况:

大型语言模型 (LLM) 可以有效地从自由文本医疗记录中提取信息.
在不需要再培训的情况下,LLM可以通过用户定义的提示来解决未见的问题.
这项研究评估了LLM用于从肺癌CT报告中提取瘤现象型.

研究的目的:

比较ChatGPT和GPT-4在数据挖掘和标记瘤表型方面的表现.
评估LLM在提取病变直径,识别转移性疾病和评估瘤进展方面的准确性.
评估LLMs的瘤推理能力和混率.

主要方法:

对424个肺癌随访CT报告 (2021年9月至2023年3月) 的回顾性研究.
在 25 份报告上进行快速工程,以指导LLM在数据提取和标记方面.
基于规则的自然语言处理管道将LLM输出与放射学家的地面真相相匹配.
在利克尔特尺度上评价LLM瘤学推理;记录了语.

主要成果:

在病变参数提取 (98.6% vs 84.0%) 和正确挖掘报告 (96% vs 67%) 中,GPT-4的表现优于ChatGPT.
在转移识别 (98.1% vs 90.3%) 和瘤进展标记 (F1评分0.96 vs 0.91) 中,GPT-4的准确性更高.
GPT-4显示出优越的瘤学推理 (利克尔特得分为4.3/4.4对3.9/3.3) 和较低的共率 (1.7%对13.7%).

结论:

在利用用户定义的提示从肺癌CT报告中提取瘤现象型方面,GPT-4显著优于ChatGPT.
GPT-4在瘤学推理中表现出增强的事实正确性和准确性,并减少了对拼的倾向.
在分析自由文本放射学报告方面,LLM,特别是GPT-4显示出提高效率和准确性的前景.

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