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.
