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A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 2: A technical introduction

Zsidai, Bálint (author)
Göteborgs universitet,University of Gothenburg,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
Kaarre, Janina (author)
Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Göteborgs universitet,University of Gothenburg,University of Pittsburgh
Narup, Eric (author)
Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Göteborgs universitet,University of Gothenburg
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Senorski, Eric Hamrin (author)
Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Göteborgs universitet,University of Gothenburg
Pareek, Ayoosh (author)
Grassi, Alberto (author)
Göteborgs universitet,University of Gothenburg
Ley, Christophe (author)
Université du Luxembourg,University of Luxembourg
Longo, Umile Giuseppe (author)
Università Campus Bio-Medico di Roma
Herbst, Elmar (author)
Hirschmann, Michael T. (author)
Kopf, Sebastian (author)
Seil, Romain (author)
Luxembourg Institute of Health
Tischer, Thomas (author)
Samuelsson, Kristian (author)
Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Göteborgs universitet,University of Gothenburg
Feldt, Robert, 1972 (author)
Chalmers tekniska högskola,Chalmers University of Technology
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 (creator_code:org_t)
2024
2024
English.
In: Journal of Experimental Orthopaedics. - 2197-1153. ; 11:3
  • Research review (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Language Technology (hsv//eng)
SAMHÄLLSVETENSKAP  -- Annan samhällsvetenskap -- Tvärvetenskapliga studier inom samhällsvetenskap (hsv//swe)
SOCIAL SCIENCES  -- Other Social Sciences -- Social Sciences Interdisciplinary (hsv//eng)

Keyword

artificial intelligence
sports medicine
orthopaedics
research methods
machine learning

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