Learning objectives:

After completing this eLearning course, you will be able to: 

• Define core AI terms (ML, DL) and describe its placement within the digital health ecosystem, including its strengths, limitations, and biases when applied to cardiovascular imaging.
• Identify high-yield tasks for AI across major cardiac imaging modalities (Echo, CMR, CT/Nuclear) and evaluate its measurable value (speed, reproducibility) in current clinical workflows.
• Summarise key regulatory requirements (e.g., EU AI Act, GDPR) and ethical principles (e.g., bias, consent, accountability) for the safe and equitable deployment of AI.
• Differentiate learning types, interpret common performance metrics (e.g., AUC, Dice), and recognize core concepts for building and validating AI models.
• Establish the necessary infrastructure (hardware, software, secure data pipelines) and devise a strategy for integrating AI into Hospital Information Systems (PACS/RIS/EHR) with human-in-the-loop oversight.

Target audience:

Cardiologists, radiologists, data-scientists, imaging specialists, researchers... with all what they should know about AI in cardiovascular imaging.