Supervised Learning in Medical Artificial Intelligence for Healthcare Professionals

Supervised Learning

 

In supervised learning, AI models learn from labelled datasets. This module covers:

 

Common Algorithms:

Learn about linear regression, logistic regression, decision trees, random forests, and support vector machines.

 

Medical Use Cases:

Explore applications such as disease risk prediction, diagnostic assistance, triage algorithms, and outcome forecasting.

 

Interpretable Models vs. Black Boxes:

Understand the importance of balancing model performance with transparency in clinical decision-making.

 

Why It Matters:

These models are often the first step in adopting AI in clinical settings. Understanding their strengths and limitations enables clinicians to interpret results wisely and effectively integrate AI into their practice.

 

CPD Awarded: 4.5 CPD credits, with approximately 4.5 hours required to complete the module plus exam time.