The DOI matches its Crossref record; 58 of the 59 references it cites were found in a registry or an index.
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 (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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Clinical information extraction applications: A literature review
Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N., Liu, S., Zeng, Y., Mehrabi, S., Sohn, S., et al. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.11.011 · Cited by 910
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The Role of Artificial Intelligence in Early Cancer Diagnosis
Hunter, B., Hindocha, S., & Lee, R. W. (2022) · Cancers · doi:10.3390/cancers14061524 · Cited by 384
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Metadata
- 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
- Health Sciences › Medicine › Health Informatics › 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
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