Evidence map›Paper›PMID 36477347›Full record

ReviewKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2023

Natural language processing: using artificial intelligence to understand human language in orthopedics.

James A Pruneski, Ayoosh Pareek, Benedict U Nwachukwu, R Kyle Martin, Bryan T Kelly, Jón Karlsson, Andrew D Pearle, Ata M Kiapour, Riley J Williams

Abstract readReview
PubMed Publisher
In one paragraph

Review in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. From patient notes to prognostication: The revolutionary potential of event-based foundation models in orthopaedics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
    Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. Artificial intelligence agents in orthopaedics: Concepts, capabilities and the road ahead.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025
    Review
  9. Review
  10. Review
  11. Article
  12. Review
  13. The Use of AI for Phenotype-Genotype Mapping.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  14. Review
  15. Article
  16. Article
  17. Review
  18. Review
  19. Article
  20. Artificial intelligence-based orthopaedic perpetual design.Journal of clinical orthopaedics and trauma · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

James A PruneskiDepartment of Orthopedic Surgery, Boston Children's Hospital, Boston, MA, USA.
Ayoosh PareekSports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, USA. ayooshp@gmail.com.ORCID http://orcid.org/0000-0001-8683-1697
Benedict U NwachukwuSports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, USA.
R Kyle MartinDepartment of Orthopedic Surgery, University of Minnesota, Minneapolis, MN, USA.
Bryan T KellySports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, USA.
Jón KarlssonDepartment of Orthopaedics, Sahlgrenska University Hospital, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.
Andrew D PearleSports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, USA.
Ata M KiapourDepartment of Orthopedic Surgery, Boston Children's Hospital, Boston, MA, USA.
Riley J WilliamsSports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Orthopedic ProceduresOrthopedicsArtificial IntelligenceHumansLanguageNatural Language ProcessingArtificial intelligenceDeep learningMachine learningNatural language processingPredictive analytics

Identifiers

PMID36477347

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.