Natural Language Processing in Oncology: A Review
Abstract
IMPORTANCE: Natural language processing (NLP) has the potential to accelerate translation of cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of personalized medicine. This technology can harvest important clinical variables trapped in the free-text narratives within electronic medical records. OBSERVATIONS: Natural language processing can be used as a tool for oncological evidence-based research and quality improvement. Oncologists interested in applying NLP for clinical research can play pivotal roles in building NLP systems and, in doing so, contribute to both oncological and clinical NLP research. Herein, we provide an introduction to NLP and its potential applications in oncology, a description of specific tools available, and a review on the state of the current technology with respect to cancer case identification, staging, and outcomes quantification. CONCLUSIONS AND RELEVANCE: More automated means of leveraging unstructured data from daily clinical practice is crucial as therapeutic options and access to individual-level health information increase. Research-minded oncologists may push the avenues of evidence-based research by taking advantage of the new technologies available with clinical NLP. As continued progress is made with applying NLP toward oncological research, incremental gains will lead to large impacts, building a cost-effective infrastructure for advancing cancer care.
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Natural language processing increases productivity in clinical oncology by converting unstructured text into analyzable data and standardizing documentation.
“Natural language processing (NLP) increases productivity in clinical oncology by converting unstructured text into analyzable data and standardizing documentation [41,42,43,44,45,46].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [43]
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- Authors
- Yim, W.-W., Yetisgen, M., Harris, W. P., & Kwan, S. W.
- Affiliations
- University of Washington Medical Center (US)
- Departments of Biomedical and Health Informatics, University of Washington Medical Center, Seattle2Departments of Linguistics, University of Washington Medical Center, Seattle
- Venue
- JAMA Oncology
- Publisher
- American Medical Association (AMA)
- Published
- 2016-06-01
- Volume
- 2
- Issue
- 6
- Pages
- 797
- ISSN
- 2374-2437 (ISSN-L)
- OpenAlex
- W2345195116
- Cited by
- 298
- References
- 59
- Citations by year
- 2026: 23 · 2025: 40 · 2024: 38 · 2023: 44 · 2022: 35 · 2021: 40 · 2020: 33 · 2019: 23 · 2018: 11 · 2017: 10 · 2016: 1
- FWCI
- 7.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA Oncology (publishedVersion)
- PubMed (publishedVersion)
- 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 › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- natural language processing, oncology, clinical natural language processing, clinical text, electronic health records, tumor staging, evidence-based oncology, quality improvement, personalized medicine, clinical research tools
- MeSH terms
- Humans, Natural Language Processing, Neoplasms, Biomedical Research, Translational Research, Biomedical, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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