Artificial Intelligence Methodology

This teaching session introduced the main machine-learning paradigms before following a medical model from training to external validation. MS examples covered diagnosis, monitoring, prognosis, subtyping and clinical text.

The question

What makes a medical AI model credible beyond the dataset on which it was developed?

Deck outline

• Supervised, unsupervised, self-supervised and reinforcement learning
• Matching model types to clinical questions and outcomes
• Training and cross-validation on development data
• Locked external validation on an independent cohort
• MS diagnosis, monitoring, prognosis and subtyping
• Bias, fairness and the route from bench to bedside

Take-away

Cross-validation supports model selection; external validation tests whether performance, calibration and fairness hold in a genuinely independent setting.