Editorial Commentary: Knowledge is Power: A Primer for Machine Learning.

Area of Science:

• Orthopaedic research
• Medical statistics
• Computational biology

Background:

• Machine learning (ML) is increasingly utilized in medical research, particularly for analyzing orthopaedic data.
• ML enables the development of adaptive predictive models for clinical patient outcomes.

Discussion:

• Online availability of ML models for physician use requires careful consideration of their validation status.
• Lack of external validation can lead to poor model generalization and inaccurate predictions.
• A rise in ML publications in medicine includes studies with significant methodological flaws.

Key Insights:

• External validation is crucial for the reliability of ML predictive models in clinical settings.
• Physicians should be aware of the potential inaccuracies in unvalidated ML models.
• Ensuring the robustness of ML algorithms is paramount for advancing evidence-based orthopaedic care.

Outlook:

• Future research should prioritize the external validation of ML models before clinical application.
• Developing standardized validation protocols for medical ML tools is essential.
• Promoting rigorous methodology in ML research will enhance its contribution to patient outcomes.