• Welcome to Week 1! This foundational module introduces the landscape of Artificial Intelligence in healthcare, with a specific focus on medical imaging.Learners will explore how AI systems are trained, how they "see" radiological images and why these tools are increasingly present in modern radiology departments.No prior technical background in AI or computing is required—the goal is conceptual clarity and professional awareness.

    🔑 Key Topics Covered:

    • Clinical AI Basics: What is AI? Machine Learning vs. Deep Learning explained clearly for clinicians.
    • Evolutionary Context: History of AI adoption in radiology and diagnostic imaging.
    • Technical Mechanisms: How convolutional neural networks (CNNs) analyze X-ray, CT, and MRI images.
    • Core Radiology Terminology: Understanding training data, model inference, sensitivity, specificity, and false positives/negatives.
    • Regulatory Awareness: Overview of FDA-cleared AI tools currently used in clinical radiology (e.g., chest X-ray triage, CT pulmonary angiography).
    • System Workflows: How AI is integrated directly into existing PACS and RIS environments.

    🎯 Week 1 Learning Outcomes:

    By the end of this week, you will be able to:

    1. Define artificial intelligence, machine learning and deep learning in plain clinical language
    2. Explain how AI models are trained using annotated imaging data
    3. Identify at least three FDA-cleared AI imaging applications and their intended clinical use cases
    4. Describe how AI outputs are typically surfaced within radiology workflows to act as your "second set of eyes"