Natural language processing in radiology: a systematic review
Abstract
Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology reports are stored for communication and documentation of diagnostic imaging, harnessing their potential requires efficient and automated information extraction: they exist mainly as free-text clinical narrative, from which it is a major challenge to obtain structured data. Natural language processing (NLP) provides techniques that aid the conversion of text into a structured representation, and thus enables computers to derive meaning from human (ie, natural language) input. Used on radiology reports, NLP techniques enable automatic identification and extraction of information. By exploring the various purposes for their use, this review examines how radiology benefits from NLP. A systematic literature search identified 67 relevant publications describing NLP methods that support practical applications in radiology. This review takes a close look at the individual studies in terms of tasks (ie, the extracted information), the NLP methodology and tools used, and their application purpose and performance results. Additionally, limitations, future challenges, and requirements for advancing NLP in radiology will be discussed.
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Deep learning methods have been used for automated generation of radiology reports, typically incorporating LSTM network models to generate textual paragraphs, and to identify findings in radiology reports.
“Recently, deep learning methods have also been used for automated generation of radiology reports , typically incorporating long-short-term-memory (LSTM) network models to generate the textual paragraphs [314, 315, 316, 317], and also to identify findings in radiology reports [318, 319, 320].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [318]
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- Authors
- Pons, E., Braun, L. M. M., Hunink, M. G. M., & Kors, J. A.
- Affiliations
- Erasmus MC (NL)
- Venue
- Radiology
- Published
- 2016-05
- Volume
- 279
- Issue
- 2
- Pages
- 329-343
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2338526423
- Cited by
- 573
- References
- 79
- Citations by year
- 2026: 41 · 2025: 34 · 2024: 69 · 2023: 70 · 2022: 82 · 2021: 85 · 2020: 70 · 2019: 56 · 2018: 36 · 2017: 27 · 2016: 3
- FWCI
- 3.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Radiology (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- natural language processing, radiology reports, clinical text mining, information extraction, structured data extraction, electronic health records, clinical decision support
- MeSH terms
- Humans, Natural Language Processing, Radiology, Radiology Information Systems, Information Storage and Retrieval, Electronic Health Records
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