Nature Biomedical Engineering
Tackling prediction uncertainty in machine learning for healthcare
- The article emphasizes the need for prediction-uncertainty metrics in healthcare applications, particularly radiology.
- It discusses the implementation of these metrics in error-intolerant and error-tolerant applications.
- It provides a framework for understanding prediction uncertainty in healthcare.
- It highlights the need for machine-learning models with zero tolerance for false-positive or false-negative errors.