Demystifying machine learning: a primer for physicians.

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Computational Biology

Background:

• Machine learning (ML) analyzes digitized datasets to optimize clinical decision-making.
• ML models identify complex patterns in large datasets for classification and prediction.
• Over 50 ML models are approved for routine healthcare, with rapid growth.

Purpose of the Study:

• To introduce principles, methods, and examples of ML in healthcare.
• To provide physicians with a foundational understanding of ML development and evaluation.
• To facilitate collaboration between clinicians and data scientists for ML model design and validation.

Main Methods:

• Narrative review of machine learning principles and applications.
• Explanation of ML model development, including data quality and feature selection.
• Discussion of ML model evaluation for safety, efficacy, and reproducibility.

Main Results:

• ML offers powerful tools for analyzing complex health data and improving predictions.
• Model reliability hinges on data quality, quantity, and appropriate feature selection.
• Physician understanding and collaboration are key to safe and effective ML implementation.

Conclusions:

• Machine learning is a transformative tool in healthcare, enhancing clinical decision-making.
• Effective ML deployment requires a solid grasp of its underlying principles and evaluation metrics.
• Interdisciplinary collaboration is essential for advancing trustworthy AI in medicine.