Related Experiment Video
Updated: Jan 7, 2026
05:33
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
738
A practical guide to the implementation of AI in orthopaedic research-Part 5: Data management
Bálint Zsidai1,2,3, Felix Oettl4, James A Pruneski5
1Sahlgrenska Sports Medicine Center Gothenburg Sweden.
Journal of Experimental Orthopaedics
|December 19, 2025
Summary
Orthopaedic research needs better data management for artificial intelligence (AI). This study outlines principles for AI data planning, collection, storage, processing, labeling, and governance in orthopaedics.
Area of Science:
- Orthopaedic Research
- Artificial Intelligence
- Data Science
Background:
- Growing volume and types of data in orthopaedic research.
- Lack of standardized data management workflows for AI in orthopaedics.
Purpose of the Study:
- Introduce principles and best practices for AI data management in orthopaedic research.
- Review data quality guidelines for medical AI and their applicability to orthopaedic datasets.
Main Methods:
- Literature review of data quality guidelines for medical AI.
- Discussion of adaptability to orthopaedic research datasets.
- Outline of future improvements for AI in orthopaedics.
Main Results:
- Essential principles for planning, collecting, storing, processing, labelling, and governing data for AI in orthopaedics.
- Evaluation of existing medical AI data quality guidelines for orthopaedic research.
Conclusions:
- Need for standardized data management workflows in AI-driven orthopaedic research.
- Identification of future directions including registry development, synthetic data, and continuous data streams.
