A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 3: How orthopaedic research benefits from the implementation of artificial intelligence
Reviewartikel, 2025
Artificial intelligence (AI) encompasses the development of systems that can perform human-like tasks, such as treatment guidance, decision-making, pattern recognition and understanding language. Within AI, machine learning and deep learning play pivotal roles in diagnosis and outcome prediction, while natural language processing aids in synthesising large datasets from the electronic medical record. In orthopaedics, AI has demonstrated success in various areas, including image evaluation, surgical planning, outcome prediction, cohort identification and administrative tasks. The purpose of this manuscript was to provide an overview of the benefits of AI implementation within the field of orthopaedics. An additional goal was to address the challenges associated with producing high quality AI-based research in a rapidly developing field. Level of Evidence: Level IV.
sports medicine
orthopaedics
research methods
artificial intelligence
machine learning
Författare
James Pruneski
Tripler Army Medical Center
Ayoosh Pareek
Hospital for Special Surgery - New York
Bálint Zsidai
Skånes universitetssjukhus (SUS)
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Jacob F. Oeding
Göteborgs universitet
Mayo Clinic
Jonathan D. Hughes
UPMC Sports Medicine
Felix C. Oettl
Universität Zürich
Philipp W. Winkler
Johannes Kepler Universität Linz (JKU)
Thomas Tischer
Universitymedicine Rostock
Malteser Waldkrankenhaus Erlangen
Elmar Herbst
University Hospital Muenster
Alberto Grassi
IRCCS Istituto Ortopedico Rizzoli, Bologna
Michael T. Hirschmann
Universität Basel
Kantonsspital Baselland
Christophe Ley
Université du Luxembourg
Yinan Yu
Göteborgs universitet
Chalmers, Data- och informationsteknik, Funktionell programmering
Kristian Samuelsson
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Journal of Experimental Orthopaedics
2197-1153 (eISSN)
Vol. 12 4 e70481Ämneskategorier (SSIF 2025)
Ortopedi
DOI
10.1002/jeo2.70481
PubMed
41180563