A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 2: A technical introduction
Review article, 2024
Recent advances in artificial intelligence (AI) present a broad range of possibilities in medical research. However, orthopaedic researchers aiming to participate in research projects implementing AI-based techniques require a sound understanding of the technical fundamentals of this rapidly developing field. Initial sections of this technical primer provide an overview of the general and the more detailed taxonomy of AI methods. Researchers are presented with the technical basics of the most frequently performed machine learning (ML) tasks, such as classification, regression, clustering and dimensionality reduction. Additionally, the spectrum of supervision in ML including the domains of supervised, unsupervised, semisupervised and self-supervised learning will be explored. Recent advances in neural networks (NNs) and deep learning (DL) architectures have rendered them essential tools for the analysis of complex medical data, which warrants a rudimentary technical introduction to orthopaedic researchers. Furthermore, the capability of natural language processing (NLP) to interpret patterns in human language is discussed and may offer several potential applications in medical text classification, patient sentiment analysis and clinical decision support. The technical discussion concludes with the transformative potential of generative AI and large language models (LLMs) on AI research. Consequently, this second article of the series aims to equip orthopaedic researchers with the fundamental technical knowledge required to engage in interdisciplinary collaboration in AI-driven orthopaedic research. Level of Evidence: Level IV.
sports medicine
orthopaedics
machine learning
artificial intelligence
research methods
Author
Bálint Zsidai
Sahlgrenska University Hospital
University of Gothenburg
Janina Kaarre
University of Gothenburg
University of Pittsburgh
Sahlgrenska University Hospital
Eric Narup
Sahlgrenska University Hospital
University of Gothenburg
Eric Hamrin Senorski
Sahlgrenska University Hospital
University of Gothenburg
Sportrehab Sports Medicine Clinic
Ayoosh Pareek
Hospital for Special Surgery
Alberto Grassi
University of Gothenburg
IRCCS Istituto Ortopedico Rizzoli, Bologna
Christophe Ley
University of Luxembourg
Umile Giuseppe Longo
Università Campus Bio-Medico di Roma
Campus Bio Medico University Hospital
Elmar Herbst
Division of General Internal Medicine
Michael T. Hirschmann
Canton Hospital Basel-Land
Sebastian Kopf
Medizinische Hochschule Brandenburg Theodor Fontane
Romain Seil
Luxembourg Institute of Research in Orthopaedics
Centre Hospitalier de Luxembourg
Luxembourg Institute of Health
Thomas Tischer
Clinic for Orthopaedics and Trauma Surgery
Kristian Samuelsson
University of Gothenburg
Sahlgrenska University Hospital
Robert Feldt
Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)
Journal of Experimental Orthopaedics
2197-1153 (eISSN)
Vol. 11 3 e12025Subject Categories (SSIF 2011)
Language Technology (Computational Linguistics)
Social Sciences Interdisciplinary
DOI
10.1002/jeo2.12025