• Welcome to Week 2! This module moves from theory into specifics—examining the categories of clinically critical findings that AI is engineered to detect and prioritize. Learners will explore the imaging hallmarks of high-acuity conditions and understand why these represent the "red flag" use cases where AI adds the most immediate clinical value as a secondary safety net.

    🔑 Key Clinical Topics Covered:

    • Clinical Priority: Understanding imaging "red flags"—urgency, missed findings and clinical consequences.

    • Musculoskeletal Flags: AI detection of occult fractures, stress fractures  and displaced fractures.

    • Pulmonary Flags: Lung nodules (Lung-RADS), consolidation, pleural effusion and pneumothorax.

    • Neurological Flags: Intracranial hemorrhage, large vessel occlusion (LVO) and midline shift.

    • Cardiovascular Flags: Cardiomegaly, aortic dilation and pulmonary edema patterns.

    • Triage Metrics: How AI scoring systems (e.g., Lung-RADS, AI triage scores) communicate clinical urgency.

    • Visualizations: Understanding confidence thresholds, heat maps and AI overlay bounding boxes.

    🎯 Week 2 Learning Outcomes:

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

    1. Name and describe at least five imaging red flags that AI tools are specifically trained to detect in acute care settings

    2. Interpret basic AI output visualizations including bounding boxes, heat maps, and confidence scores accurately

    3. Differentiate between high-sensitivity AI tools (designed to catch more findings) vs. high-specificity tools (designed to minimize false alarms)

    4. Relate the concept of a "missed finding" to the value of AI acting as a robust redundancy layer in your daily imaging review workflow.