Natural language processing: using artificial intelligence to understand human language in orthopedics
Abstract
Natural language processing (NLP) describes the broad field of artificial intelligence by which computers are trained to understand and generate human language. Within healthcare research, NLP is commonly used for variable extraction and classification/cohort identification tasks. While these tools are becoming increasingly popular and available as both open‐source and commercial products, there is a paucity of the literature within the orthopedic space describing the key tasks within these powerful pipelines. Curation and navigation of the electronic medical record are becoming increasingly onerous, and it is important for physicians and other healthcare professionals to understand potential methods of harnessing this large data resource. The purpose of this study is to provide an overview of the tasks required to develop an NLP pipeline for orthopedic research and present recent examples of successful implementations.
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In orthopedics, deep learning has improved musculoskeletal diagnosis and surgical procedures, while natural language processing has simplified communication between physicians and patients.
“In orthopedics, deep learning has improved musculoskeletal diagnosis and surgical procedures, while natural language processing has simplified communication between physicians and patients.1314)”
From Application of ChatGPT for Orthopedic Surgeries and Patient Care (2024) · cites this paper as [14]
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- Authors
- Pruneski, J. A., Pareek, A., Nwachukwu, B. U., Martin, R. K., Kelly, B. T., Karlsson, J., Pearle, A. D., Kiapour, A. M., & Williams, R. J.
- Affiliations
- Boston Children's Hospital (US)
- Hospital for Special Surgery (US)
- University of Minnesota (US)
- Sahlgrenska University Hospital (SE)
- Corresponding author
- Pareek, Ayoosh
- ORCID
- Venue
- Knee Surgery, Sports Traumatology, Arthroscopy
- Publisher
- Wiley
- Published
- 2022-12-07
- Volume
- 31
- Issue
- 4
- Pages
- 1203-1211
- ISSN
- 0942-2056 (ISSN-L)
- 1433-7347
- OpenAlex
- W4310959348
- Cited by
- 50
- References
- 45
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 16 · 2023: 7 · 2022: 1
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Knee Surgery Sports Traumatology Arthroscopy (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 › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- natural language processing, orthopaedic research, electronic health records, clinical text mining, variable extraction, cohort identification
- MeSH terms
- Artificial Intelligence, Humans, Language, Natural Language Processing, Orthopedics, Orthopedic Procedures
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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