ArticleBMJ open2023
Large language model-based information extraction from free-text radiology reports: a scoping review protocol.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A comparative study of screening performance between abstrackr and GPT models: Systematic review and contextual analysis.BMC medical informatics and decision making · 2025Pooled it
- Automated chest X-ray disease screening using large language models and deep convolutional neural networks on the MIMIC-CXR dataset.Frontiers in digital health · 2026Article
- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- Automated real-time assessment of intracranial hemorrhage detection AI using an ensembled monitoring model (EMM).NPJ digital medicine · 2025Article
- Open-Source Hybrid Large Language Model Integrated System for Extraction of Breast Cancer Treatment Pathway From Free-Text Clinical Notes.JCO clinical cancer informatics · 2025Article
- Enhancing Bidirectional Encoder Representations From Transformers (BERT) With Frame Semantics to Extract Clinically Relevant Information From German Mammography Reports: Algorithm Development and Validation.Journal of medical Internet research · 2025Article
- Comprehensive testing of large language models for extraction of structured data in pathology.Communications medicine · 2025Article
- Revolutionizing Radiology with Natural Language Processing and Chatbot Technologies: A Narrative Umbrella Review on Current Trends and Future Directions.Journal of clinical medicine · 2024Review
- Integrating large language models in care, research, and education in multiple sclerosis management.Multiple sclerosis (Houndmills, Basingstoke, England) · 2024Review
- A scoping review of large language model based approaches for information extraction from radiology reports.NPJ digital medicine · 2024Article
- Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.Yearbook of medical informatics · 2024Article
- Leveraging Large Language Models for Thyroid Nodule Information Extraction and Matching Across Medical Reports.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials.Informatics in medicine unlocked · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionRadiological 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. METHODS AND ANALYSIS: This protocol is designed based on the 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.