AI Literacy for Healthcare Professionals
A structured, evidence-based course designed for clinicians, nurses, allied health professionals, and healthcare leaders who need to understand, evaluate, and safely work with artificial intelligence in clinical settings — no coding or technical background required.
What You Will Learn
Who This Course Is For
This course is built for practising clinicians and healthcare professionals — doctors, nurses, pharmacists, allied health professionals, medical students, and healthcare managers — who encounter AI tools in their work and need a reliable framework to evaluate, question, and safely use them. No prior knowledge of AI, statistics, or programming is required.
Course Modules
Module 1: What AI Really Is (And Isn't): A Clinician's First Look
○An accessible introduction to how AI differs from traditional software and why clinicians must understand its foundations and limitations.
Module 2: How AI Learns (and Why It Sometimes Gets It Wrong)
○A practical look at how machine learning models develop, where they can go astray, and how data quality shapes clinical performance.
Module 3: How AI Performs in Clinical Practice: Real Cases, Real Limits
○Evidence-based walkthroughs of landmark clinical AI studies, revealing how success depends on data, labels, validation, and context.
Module 4: Trustworthy AI: Ethics, Risk, and Regulation in Clinical Practice
○A guide to understanding ethical principles, identifying bias, and navigating the regulatory and professional responsibilities clinicians share when using AI.
Module 5: Evaluating & Validating AI Tools in Clinical Practice
○How to critically appraise AI vendor claims, interpret performance metrics, understand regulatory clearance, and ask the right questions before adopting any AI tool.
Module 6: AI Governance, Regulation & Institutional Policy
○A practical guide to the FDA SaMD framework, EU AI Act, hospital AI governance committees, audit trails, and what institutional policy must cover.
Module 7: Working with Large Language Models (LLMs) in Clinical Practice
○Safe and effective use of ChatGPT/Claude-style tools in healthcare — covering hallucinations, prompt engineering, data protection risks, and institutional governance.
Module 8: AI Implementation & Change Management in Healthcare
○How to lead or participate in an AI deployment — covering workflow redesign, stakeholder engagement, staff training, post-deployment monitoring, and when to halt a deployment.