Large language model-based information extraction from free-text radiology reports: a scoping review protocol

Abstract

Introduction Radiological imaging is one of the most frequently performed diagnostic tests worldwide. The free text contained in radiology reports is currently only rarely utilized for secondary use, 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. 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 nor scoping review of IE of radiology reports, based on LLMs, has been conducted yet. Existing publications are outdated and do not comprise LLM-based models. Methods and analysis 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, ACM Digital Library) are defined. Furthermore, we describe the screening process, data charting, analysis and presentation of extracted data. Ethics and dissemination 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.

SEETrials: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trials

“…Within the realm of information extraction from clinical documents, various LLMs have been deployed in diverse applications, such as analyzing radiology reports [17–19] and electronic health records [15]. In the clinical trial domain, LLMs play crucial roles in trial information retrieval [20], criteria text generation [21], and clinical trial eligibility criteria analysis [22].…”

“…Generative Pre-trained Transformer (GPT) models, a subset of large language models (LLMs), have shown exceptional proficiency in contextual understanding and textual data processing [15][16][17][18] . Various GPT models have been examined for information extraction from clinical documents 16,[19][20][21] . In the clinical trial domain, LLMs show promise in trial information retrieval 22 , criteria text generation 23 , and clinical trial eligibility criteria analysis 24 .…”