Artificial Intelligence in Radiology will provide imagers of all experience levels with the introductory information and foundational skills necessary to thrive in a future impacted by AI integrated healthcare. Developed in collaboration with Artificial Intelligence Medical Education (AiME), this activity is designed to demonstrate AI’s radiology applications, its potential to enhance clinical practice and its limitations. As such, this practical and clinically relevant overview will detail the strengths and weaknesses of this emerging technology
Artificial Intelligence in Radiology
Session 1: Big Picture AI | |
Overview, Terms and Methods | Jordan Perchik, MD |
Preparing for the Future of AI in Radiology | Jordan Perchik, MD |
Session 2: Radiology Appilcations I | |
AI Applications in Neuro Imaging | Jordan Perchik, MD |
AI Applications in Breast Imaging | Jordan Perchik, MD |
AI Applications in Abdominal Imaging | Jordan Perchik, MD |
AI Applications in Cardiothoracic Imaging | Jordan Perchik, MD |
Session 3: Radiology Applications II | |
AI Applications in Nuclear Imaging | Jordan Perchik, MD |
AI Applications in Pediatric Imaging | Jordan Perchik, MD |
AI Applications in MSK Imaging | Jordan Perchik, MD |
AI and Workflow Optimization | Jordan Perchik, MD |
Session 4: Economics, Ethics and the Marketplace | |
Economics and Ethics of Radiology AI | Jordan Perchik, MD |
The AI Marketplace | Jordan Perchik, MD |
Evaluating an AI Application | Jordan Perchik, MD |
Session 5: Quality Assurance, Medicolegal Considerations and Education | |
Quality Assurance in Clinical AI | Jordan Perchik, MD |
Medicolegal Considerations in Clinical AI | Jordan Perchik, MD |
State of AI Education in Radiology | Jordan Perchik, MD |
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