Emergency Imaging AI Certificate Program (2024)

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Keyword(s) CPAI0124, Certificate Program, Artificial Intelligence, Emergency Radiology

Tag(s) Certificate Program, Artificial Intelligence, Emergency Radiology

Description

The RSNA’s Emergency Imaging AI Certificate Program is an on-demand, self-paced course intended to support radiologists in using artificial intelligence in the high-stakes environment of an emergency setting. It consists of six modules of case-based and hands-on curriculum featuring expert instructors.

Content Areas (Codes):
The following Content Areas will be printed on the certificate for this course:

Artificial Intelligence
Emergency Radiology

Learning Objectives:
By the conclusion of this emergency radiology AI program, you will have information to support every phase of emergency imaging AI from assessment to use case evaluation to implementation:

Gain a comprehensive understanding of assessing AI models through clinical, technical, and business perspectives.
Examine various AI applications using multiple use cases in emergency radiology.
Identify critical factors for the successful implementation of emergency radiology AI models.

Start Date: 01/17/2024
Online Expiration Date: 06/25/2027

Price:
Non-Member/Basic Member Rate: $1,050.00
Standard Member/Full Access Member Rate: $850.00

Refund / Exchange Policy:
RSNA will not issue any refunds or exchanges for online only versions of educational products or activities purchased online. Please review the entire product or activity description prior to purchase.

RSNA Disclaimer:
The opinions or views expressed in this activity are those of the presenters and do not necessarily reflect the opinions, recommendations or endorsement of the RSNA. Participants should critically appraise the information presented and are encouraged to consult appropriate resources for information surrounding any product or device mentioned. Information presented, as well as publications, technologies, products and/or services discussed, are intended to inform the learner about the knowledge, techniques, and experiences of RSNA faculty who are willing to share such information with colleagues. The RSNA disclaims any and all liability for damages to any individual user for all claims which may result from the use of said information, publications, technologies, products and/or services, and events.

See Module Details Below

Module 1

Learning Objectives:

Describe important variables when gathering medical imaging data in local and multi-centered settings.
Identify evolution of NLP and how it improves the efficiency of clinical workflows.
Recognize terms associated with AI in healthcare, and why workflow considerations are essential prior to an AI solution being deployed.

This Other Activity (blended enduring material and simulation) is estimated to take 1 hour and 30 minutes to complete. This activity has not been designated for continuing medical education credit.

Faculty:

Po-Hao (Howard) Chen, MD, MBA
Tessa S. Cook MD, PhD, CIIP, FSIIM, FCPP
Marta Heilbrun, MD
Amy Kotsenas, MD
Nina Kottler, MD, MS
Luciano Prevedello, MD, MPH
Nabile M. Safdar, MD, MPH

Module 2

Learning Objectives:

Summarize best practices on how to approach the technical evaluation of an AI tool.
Apply relevant statistics and best practices when assessing Radiology AI clinical application tools.
Examine crucial elements for identifying the suitability of an AI application from a value perspective.

This Other Activity (blended enduring material and simulation) is estimated to take 1 hour and 30 minutes to complete. This activity has not been designated for continuing medical education credit.

Faculty:

Bernardo C. Bizzo, MD, PhD
Melissa Davis, MD, MBA
Florence Doo, MD, MA
Chintan Shah, MD, MS
Axel W. E. Wismueller, MD, M.Sc, PhD

Module 2B

This Other Activity (blended enduring material and simulation) is estimated to take 1 hour to complete. This activity has not been designated for continuing medical education credit.

Learning Objectives:
Implement three experiments aimed at training AI models to deepen your comprehension of its capabilities and challenges.

Faculty:

Luciano Prevedello, MD, MPH

Module 3

Learning Objectives:

Describe CADt products and their use in radiology workflow for clinical practice.
Interpret the value of AI products to the various stakeholders in the imaging value chain at your institution.
Assess available FDA cleared AI Algorithms for suitability to your clinical practice.

This Other Activity (blended enduring material and simulation) is estimated to take 1 hour and 30 minutes to complete. This activity has not been designated for continuing medical education credit.

Faculty:

Bibb Allen Jr. MD, FACR
Namita S. Gandhi, MD MScHI FSAR
Sriyesh Krishnan, MD
Naveen Subhas MD, MPH

Module 4

Learning Objectives:

Identify and distinguish between different care coordination pathways and explain the role of AI-based automation in enhancing care coordination.
Articulate the significance of radiologists' role in care coordination and develop strategies that are important to recommended follow-up care for patients.

This Other Activity (blended enduring material and simulation) material is estimated to take 1 hour to complete. This activity has not been designated for continuing medical education credit.

Faculty:

Anjali Balagopal, MBE, PhD
Melissa Chen, MD
Nina Kottler MD, MS
Sriyesh Krishnan, MD