A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 2: A technical introduction.
artificial intelligence
machine learning
orthopaedics
research methods
sports medicine
Journal
Journal of experimental orthopaedics
ISSN: 2197-1153
Titre abrégé: J Exp Orthop
Pays: United States
ID NLM: 101653750
Informations de publication
Date de publication:
Jul 2024
Jul 2024
Historique:
received:
13
12
2023
revised:
31
01
2024
accepted:
21
03
2024
medline:
8
5
2024
pubmed:
8
5
2024
entrez:
8
5
2024
Statut:
epublish
Résumé
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 IV.
Identifiants
pubmed: 38715910
doi: 10.1002/jeo2.12025
pii: JEO212025
pmc: PMC11076014
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
e12025Informations de copyright
© 2024 The Author(s). Journal of Experimental Orthopaedics published by John Wiley & Sons Ltd on behalf of European Society of Sports Traumatology, Knee Surgery and Arthroscopy.
Déclaration de conflit d'intérêts
Michael T. Hirschmann is a consultant for Medacta, Symbios and Depuy Synthes. Kristian Samuelsson is a member on the board of directors for Getinge AB (publ). Robert Feldt is Chief Technology Officer and founder in Accelerandium AB, a software consultancy company.