Unsupervised Learning in Medical Artificial Intelligence for Healthcare Professionals

Unsupervised learning uncovers hidden patterns in unlabeled data. In this module, you will explore:

Clustering:

Techniques for patient segmentation, phenotype discovery, and care pathway mapping.

Dimensionality Reduction:

Methods like PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) to simplify complex datasets.

Anomaly Detection:

Identifying unusual trends, rare conditions, or system errors.

Why It Matters:

These techniques can reveal insights clinicians may overlook, supporting personalised care delivery and operational efficiency.

CPD Awarded: 3.5 CPD, with approximately 3.5 hours required to complete the module plus exam time.