Learning Outcomes

In this free course, you will learn how to:

• Explain how AI has evolved in clinical practice and supports diagnosis, treatment, and patient engagement
• Apply the R.O.A.D. Framework to evaluate readiness and guide strategic AI deployment in healthcare organisations
• Distinguish between rule-based systems and machine learning approaches in healthcare AI applications
• Evaluate AI model performance using accuracy, sensitivity, precision, and cross-validation techniques
• Compare decision trees, logistic regression, and ensemble methods for healthcare prediction tasks
• Describe how large language models automate clinical documentation and reduce administrative burden
• Analyse how AI-powered predictive analytics detects early warning signals in hospitalised patients
• Assess ethical frameworks for equitable access, algorithmic fairness, and transparency in AI governance
• Identify implementation risks, including data drift, workflow misalignment, and clinician disengagement
• Plan phased deployment strategies and validation protocols for safe AI rollout in clinical settings
• Evaluate leadership styles and social network strategies that support AI transformation in healthcare
• Discuss how federated learning, multimodal AI, and policy frameworks shape global health innovation