AI has the potential to become a necessary tool for any radiologist in the near future. In this microlearning complement to the RSNA Spotlight Course, “AI Implementation: Building Expertise and Influence,” you’ll gain the basic knowledge you need to understand and consider AI’s capabilities, advantages, and limitations when evaluating AI products that you want to integrate into your practice.
To learn more about the RSNA’s AI Education, specifically the Imaging AI Certificate Program, please click here.
Content Areas (Codes):
The following Content Areas will be printed on the certificate for this course:
Why should I participate in this activity?
You are interested in using AI to support your radiology practice, but, like many people, find yourself unsure of where to start when it comes to learning about it. This introduction to deep learning and its applications in radiology is meant to serve as a roadmap for that exact situation. This course can help you to decide where you want to go with AI by beginning to answer three important questions:
- What can deep learning do?
- How does deep learning help a radiologist?
- What pitfalls should I look out for when using AI in radiology?
This non-CME enduring material is estimated to take 15 minutes to complete.
Start Date: 4/26/2023
Online Expiration Date: 4/25/2026
Faculty:
Price:
Non-member Rate: $0.00
Member Rate: $0.00
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.