PDF(Pubmed)
Abstract：
Radiological imaging is one of the most frequently performed diagnostic tests worldwide. The free-text contained in radiology reports is currently only rarely used for secondary use purposes, including research and predictive analysis. However, this data might be made available by means of information extraction (IE), based on natural language processing (NLP). Recently, a new approach to NLP, large language models (LLMs), has gained momentum and continues to improve performance of IE-related tasks. The objective of this scoping review is to show the state of research regarding IE from free-text radiology reports based on LLMs, to investigate applied methods and to guide future research by showing open challenges and limitations of current approaches. To our knowledge, no systematic or scoping review of IE from radiology reports based on LLMs has been published. Existing publications are outdated and do not comprise LLM-based methods.
This protocol is designed based on the JBI Manual for Evidence Synthesis, chapter 11.2: \'Development of a scoping review protocol\'. Inclusion criteria and a search strategy comprising four databases (PubMed, IEEE Xplore, Web of Science Core Collection and ACM Digital Library) are defined. Furthermore, we describe the screening process, data charting, analysis and presentation of extracted data.
This protocol describes the methodology of a scoping literature review and does not comprise research on or with humans, animals or their data. Therefore, no ethical approval is required. After the publication of this protocol and the conduct of the review, its results are going to be published in an open access journal dedicated to biomedical informatics/digital health.
This protocol is designed based on the JBI Manual for Evidence Synthesis, chapter 11.2: \'Development of a scoping review protocol\'. Inclusion criteria and a search strategy comprising four databases (PubMed, IEEE Xplore, Web of Science Core Collection and ACM Digital Library) are defined. Furthermore, we describe the screening process, data charting, analysis and presentation of extracted data.
This protocol describes the methodology of a scoping literature review and does not comprise research on or with humans, animals or their data. Therefore, no ethical approval is required. After the publication of this protocol and the conduct of the review, its results are going to be published in an open access journal dedicated to biomedical informatics/digital health.
摘要：
背景：放射成像是全球范围内最常用的诊断测试之一。放射学报告中包含的自由文本目前很少用于次要用途。包括研究和预测分析。然而,这些数据可以通过信息提取(IE)提供，基于自然语言处理(NLP)。最近,一种新的NLP方法，大型语言模型(LLM)，已经获得了动力，并继续提高IE相关任务的性能。TheobjectiveofthisscopingreviewistoshowthestateofresearchregardingIEfromfree-textradiologyreportsbasedonLLM,研究应用的方法，并通过展示当前方法的开放挑战和局限性来指导未来的研究。据我们所知,尚未发表基于LLM的放射学报告对IE进行系统或范围审查。现有出版物已过时，不包含基于LLM的方法。
方法：该协议是根据JBI证据综合手册设计的，第11.2章：“范围审查协议的制定”。纳入标准和包含四个数据库的搜索策略(PubMed，IEEEXplore,WebofScience核心馆藏和ACM数字图书馆）已定义。此外，我们描述了筛选过程，数据图表，提取数据的分析和呈现。
背景：该协议描述了范围界定文献综述的方法，不包括对人类或与人类的研究，动物或他们的数据。因此,不需要道德批准。在本协议公布和审查进行后，其结果将发表在致力于生物医学信息学/数字健康的开放获取期刊上。
方法：该协议是根据JBI证据综合手册设计的，第11.2章：“范围审查协议的制定”。纳入标准和包含四个数据库的搜索策略(PubMed，IEEEXplore,WebofScience核心馆藏和ACM数字图书馆）已定义。此外，我们描述了筛选过程，数据图表，提取数据的分析和呈现。
背景：该协议描述了范围界定文献综述的方法，不包括对人类或与人类的研究，动物或他们的数据。因此,不需要道德批准。在本协议公布和审查进行后，其结果将发表在致力于生物医学信息学/数字健康的开放获取期刊上。
