A practical guide to the implementation of AI in orthopaedic research-Part 5: Data management
Reviewartikel, 2025
While the magnitude and types of data available to orthopaedic researchers are steadily growing, standardized and efficient data management workflows for orthopaedic research using artificial intelligence (AI) are currently lacking. This work introduces essential principles and best practices for planning, collecting, storing, processing, labelling and governing data in AI-based orthopaedic research. The various domains of available data quality guidelines for medical AI research are reviewed and discussed in terms of their adaptability to orthopaedic research datasets. In addition, future areas of improvement, such as registry development, the potential of synthetic data and gradual transition to continuous data streams for AI applications, are outlined.Level of Evidence Level V.
methods
machine learning
causal inference
data analysis
artificial intelligence
Författare
Balint Zsidai
Göteborgs universitet
Felix Oettl
Universität Zürich
James A. Pruneski
Tripler Army Medical Center
Gergely Panics
FIFA Medical Centre of Excellence (FMCE)
Semmelweis Egyetem
Philipp W. Winkler
Göteborgs universitet
Eric Hamrin Senorski
Göteborgs universitet
Michael T. Hirschmann
Kantonsspital Baselland
Yinan Yu
Chalmers, Data- och informationsteknik, Funktionell programmering
Robert Feldt
Chalmers, Data- och informationsteknik, Software Engineering
Kristian Samuelsson
Göteborgs universitet
Journal of Experimental Orthopaedics
2197-1153 (eISSN)
Vol. 12 4 e70581Ämneskategorier (SSIF 2025)
Ortopedi
Artificiell intelligens
DOI
10.1002/jeo2.70581
PubMed
41416245