HSLC Room 1244 and virtual: https://uwmadison.zoom.us/j/92284183932?pwd=Jv6trXPGjTNe5g7EYaFQWEz9AUmjeU.1

There is no registration prior to the session. Virtual attendees can use the Zoom link provided above to attend. Instructions for recording attendance and claiming credit will be provided during the session.

Learning Objectives

As a result of participation in this educational session, learners as members of healthcare or research team, will improve their ability to

  1. Identify key opportunities for artificial intelligence (AI) in the radiology process lifecycle.
  2. Choose a general AI algorithm framework to solve problems based on the type of data available for input and output.
  3. Define foundation models (including the role of embeddings), contrast them with traditional AI, and describe their capabilities, limitations, and potential clinical applications in radiology via adaptation.
  4. Critically assess clinical use cases derived from foundation models, performance evidence, safety considerations, workflow integration strategies, and the impact on radiologist decision-making and efficiency.

Presenters

  • Joshua D. Warner, MD, PhD, CIIP
  • Alan B. McMillan, PhD

PDF icon AI_GR_Session2_April9_Infosheet_20250409.pdf

Session date: 
04/09/2025 - 12:15pm to 1:15pm CDT
Location: 
Virtually & In Person Hybrid
Madison, WI
United States
See map: Google Maps
  • 1.00 AAPA Category 1 CME
  • 1.00 ABS Accredited CME
  • 1.00 AMA PRA Category 1 Credit™
  • 1.00 ANCC Contact Hours
  • 1.00 University of Wisconsin–Madison Continuing Education Hours
    • 1.00 Approved for AMA PRA Category 1 Credit™
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