ArticleJournal of digital imaging2018
Automated Radiology Report Summarization Using an Open-Source Natural Language Processing Pipeline.
Article in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.Acta neurochirurgica. Supplement · 2022Pooled it
- Less Is More: Radiologists Prefer Brief Narratives in LLM-Generated Clinical History Summaries.Journal of imaging informatics in medicine · 2026Article
- Review
- Diagnostic report generation for macular diseases by natural language processing algorithms.The British journal of ophthalmology · 2025Article
- Radiology Report Annotation Using Generative Large Language Models: Comparative Analysis.International journal of biomedical imaging · 2025Article
- Automatic generation of conclusions from neuroradiology MRI reports through natural language processing.Neuroradiology · 2024Article
- Patient Information Summarization in Clinical Settings: Scoping Review.JMIR medical informatics · 2023Article
- Leveraging physiology and artificial intelligence to deliver advancements in health care.Physiological reviews · 2023Review
- Artificial Intelligence to Improve Patient Understanding of Radiology Reports.The Yale journal of biology and medicine · 2023Review
- Evaluation of a Concept Mapping Task Using Named Entity Recognition and Normalization in Unstructured Clinical Text.Journal of healthcare informatics research · 2020Article
- Ontology-Based Radiology Teaching File Summarization, Coverage, and Integration.Journal of digital imaging · 2020Article
- Implementing Structured Clinical Templates at a Single Tertiary Hospital: Survey Study.JMIR medical informatics · 2020Article
- Virus database annotations assist in tracing information on patients infected with emerging pathogens.Informatics in medicine unlocked · 2020Article
- Identifying incidental findings from radiology reports of trauma patients: An evaluation of automated feature representation methods.International journal of medical informatics · 2019Article
- Classification of Pulmonary Nodular Findings based on Characterization of Change using Radiology Reports.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnostic radiologists are expected to review and assimilate findings from prior studies when constructing their overall assessment of the current study. Radiology information systems facilitate this process by presenting the radiologist with a subset of prior studies that are more likely to be relevant to the current study, usually by comparing anatomic coverage of both the current and prior studies. It is incumbent on the radiologist to review the full text report and/or images from those prior studies, a process that is time-consuming and confers substantial risk of overlooking a relevant prior study or finding. This risk is compounded when patients have dozens or even hundreds of prior imaging studies. Our goal is to assess the feasibility of natural language processing techniques to automatically extract asserted and negated disease entities from free-text radiology reports as a step towards automated report summarization. We compared automatically extracted disease mentions to a gold-standard set of manual annotations for 50 radiology reports from CT abdomen and pelvis examinations. The automated report summarization pipeline found perfect or overlapping partial matches for 86% of the manually annotated disease mentions (sensitivity 0.86, precision 0.66, accuracy 0.59, F1 score 0.74). The performance of the automated pipeline was good, and the overall accuracy was similar to the interobserver agreement between the two manual annotators.
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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.