AI in Diagnostic Imaging: What Every R.T. Needs to Know

This continuing education course provides radiologic technologists with a comprehensive, clinically grounded understanding of artificial intelligence (AI) as it applies to diagnostic medical imaging. The course covers the foundational concepts of AI, machine learning, and deep learning, traces the history of AI in radiology from first-generation computer-aided detection through the deep learning era, and examines the current regulatory framework governing AI medical devices. Detailed modality-specific sections explore how AI is being applied in radiography, computed tomography, mammography, magnetic resonance imaging, ultrasound, and nuclear medicine, with emphasis on the products in clinical use, the evidence behind them, and their impact on the technologist's workflow.

Approvals & Acceptance

• ✓
  ASRT Approved, Category A

  Accepted by the ARRT^® toward your CE requirement
• ✓
  ARDMS Accepted

  Counts for ARDMS credential holders
• ✓
  NMTCB Accepted

  Counts for NMTCB credential holders
• ✓
  California

  Accepted for CRTs, XTs, Supervisors and Operators
• ✓
  Florida

  Approved for Technical Category A for Florida technologists
• ✓
  Texas: Directly Related

  Counts toward the directly related hours Texas MRTs need (at least 12 of 24)
• ✓
  We Report for You: Florida, Texas, Kansas and New Hampshire

  Completions are reported in the first week of each month for the previous month: Florida directly to the Florida Department of Health, Bureau of Radiation Control; Texas, Kansas and New Hampshire to CE Broker. Your state license number must be entered in your dashboard for us to report your credits.
• ✓
  All other states

  Accepted

Applicable Disciplines

Learning Objectives

• 1Define artificial intelligence, machine learning, and deep learning as they apply to medical imaging, and distinguish between these terms in clinical contexts.
• 2Describe the historical development of AI in radiology, from computer-aided detection (CAD) to modern convolutional neural networks (CNNs).
• 3Identify at least five FDA-cleared AI applications currently used in diagnostic imaging across multiple modalities.
• 4Explain how AI algorithms are trained using imaging datasets and recognize the significance of training data quality, bias, and validation.
• 5Describe the radiologic technologist's role in AI-integrated workflows, including image acquisition optimization, quality assurance, and data input accuracy.
• 6Discuss patient safety considerations specific to AI-assisted imaging, including over-reliance on algorithms, false positives and negatives, and informed consent.
• 7Analyze the ethical and legal implications of AI in diagnostic imaging, including liability, algorithmic bias, health equity, and data privacy (HIPAA compliance).
• 8Evaluate the impact of AI on imaging workflow efficiency, including automated protocoling, dose optimization, image reconstruction, and report generation.
• 9Recognize how AI is being applied in specific modalities including radiography, CT, MRI, mammography, ultrasound, and nuclear medicine.
• 10Summarize the current regulatory framework for AI in medical imaging, including the FDA's clearance pathways and the role of professional organizations.
• 11Discuss the evolving job market for radiologic technologists in an AI-integrated healthcare environment and identify strategies for professional growth.
• 12Apply critical thinking when evaluating AI tool performance claims and vendor marketing in the imaging department.

Course Outline

• •Clearing Up the Buzzwords
• •How We Got Here, A Brief History of AI in Radiology
• •FDA Regulation of AI in Medical Imaging
• •AI in Radiography and Computed Tomography
• •AI in Mammography and Breast Imaging
• •AI in MRI, Ultrasound, and Nuclear Medicine
• •The R.T.'s Role in an AI-Integrated Department
• •Patient Safety in the Age of AI
• •Ethics, Bias, and Health Equity
• •Evaluating AI Tools, A Critical Thinking Guide for Techs
• •Your Career in an AI-Enhanced World
• •Looking Ahead, The Next Five Years