An interactive educational tool for teaching clinicians how to interpret AI diagnostic metrics using a chest X-ray pneumonia detection example.
This tutorial helps clinicians understand:
- Confusion Matrix: True positives, false positives, true negatives, false negatives
- Key Metrics: Sensitivity (recall), specificity, precision (PPV), accuracy, F1 score
- ROC and PR Curves: How threshold selection affects the tradeoff between metrics
- Prevalence Effects: Why the same AI performs differently in high vs. low prevalence settings
- Interactive threshold slider to explore metric tradeoffs
- Three clinical scenarios: Severe Cases, Subtle Cases, and Low Prevalence
- Toggle between ROC and Precision-Recall curves
- Adjustable prevalence slider to demonstrate Bayesian effects on precision
- Clinical scenario questions with expandable answers
Open index.html in a web browser. No build step or server required.
- Vishnu Ravi, MD
- Alaa Youssef, PhD
- Aydin Zahedivash, MD
- Gabriel Tse, MD
- Jonathan Chen, MD, PhD
Stanford Medicine - AI in Medical Education