• Welcome to Week 4! This module invites learners to examine the professional, ethical, and regulatory landscape surrounding AI in medical imaging. As AI continues to evolve rapidly, Radiologic Technologists must be equipped to think critically about accountability, algorithmic bias, patient safety and evolving professional identity.

    This week provides structural frameworks for the responsible and informed use of AI tools in clinical settings.

    🔑 Key Ethical & Regulatory Topics Covered:

    * Professional Identity: The role of the RT in an AI-augmented radiology department—focusing on collaboration, not replacement.

    * Regulatory Compliance: FDA oversight of AI/ML-based medical devices, exploring the 510(k) review pathway and Software as a Medical Device (SaMD) classifications.

    * Algorithmic Bias: Exploring bias in medical imaging AI—analyzing underlying causes, documented clinical examples and modern mitigation strategies.

    * Data Privacy: Patient privacy safeguards, cloud data security protocols and informed consent considerations when using AI imaging tools.

    * Legal & Ethical Liability: Error accountability frameworks—determining who is responsible when AI contributes to a missed or incorrect diagnosis.

     * Clinical Communication: Best practices for transparently communicating AI boundaries and technical limitations to patients and clinical colleagues.

    🎯 Week 4 Learning Outcomes:

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

    1. Explain the FDA’s regulatory pathways and criteria for approving AI-based medical imaging software

     2. Identify at least two documented examples of algorithmic bias in medical imaging AI and explain their direct clinical impact on diverse patient populations

     3. Articulate an informed, evidence-supported professional position on the ethical use of AI tools in radiologic technology practice.

    4. Describe accountability and liability frameworks for RT professionals when AI tools are actively involved in patient care decisions.