Cited by 108Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsTopic Modeling

A deeper dive into ChatGPT: history, use and future perspectives for orthopaedic research

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Ollivier, Matthieu, Pareek, Ayoosh, Dahmen, Jari, Kayaalp, M. Enes, Winkler, Philipp W., Hirschmann, Michael T., Karlsson, Jon·2023·Knee Surgery, Sports Traumatology, Arthroscopy

Abstract

The rate of plagiarism and false content in scientific literature varies depending on the field of study and the methods used to detect it.According to some studies, the overall rate of plagiarism in scientific literature is estimated to be around 2-3% [1].However, the rate can be higher in certain fields and for certain types of content.Additionally, the rate of false or fraudulent content in scientific literature is difficult to quantify, as it often goes undetected or is not reported.However, cases of scientific misconduct, including the fabrication and falsification of data, have been reported in various fields and can have serious consequences for both the authors and the scientific community.It is important for the scientific community to maintain high standards of ethics and accuracy in scientific research to ensure the validity and reliability of the published literature.In this editorial, we will touch upon the description of Large Language Models, define their limits and strengths and finally explore options to detect fraudulent manuscripts.Large Language Models (LLM) have the potential to assist researchers in generating clear and concise writing, summarising vast amounts of information and performing various language-related tasks [2].This can potentially save

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References 11 · Cited by 108

Authors
Ollivier, M., Pareek, A., Dahmen, J., Kayaalp, M. E., Winkler, P. W., Hirschmann, M. T., & Karlsson, J.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • Hospital for Special Surgery (US)
  • Academic Center for Evidence-Based Sports Medicine (NL)
  • Dr Lütfi Kırdar Kartal Eğitim ve Araştırma Hastanesi (TR)
  • Johannes Kepler University of Linz (AT)
  • Kantonsspital Baselland (CH)
  • <!--<label>9</label>--> Department of Orthopaedics, Institute of Clinical Sciences Sahlgrenska University Hospital, Sahlgrenska Academy, Gothenburg University Gothenburg Sweden
Corresponding author
Ollivier, Matthieu
Venue
Knee Surgery, Sports Traumatology, Arthroscopy
Publisher
Wiley
Published
2023-03-09
Volume
31
Issue
4
Pages
1190-1192
ISSN
  • 0942-2056 (ISSN-L)
  • 1433-7347
OpenAlex
W4323811257
Cited by
108
References
11
Citations by year
2026: 3 · 2025: 21 · 2024: 44 · 2023: 39 · 2012: 1
Type
journal article
Language
en
Open access
Locations
  • Knee Surgery Sports Traumatology Arthroscopy (publishedVersion)
  • PubMed (publishedVersion)
  • Pure Amsterdam UMC (acceptedVersion, cc-by)
  • HAL (submittedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
ChatGPT, large language models, artificial intelligence, orthopaedic research, future perspectives
MeSH terms
Diving, Humans, Orthopedics

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