The RSNA Imaging AI Advanced Certificate course is case-based and consists of six modules featuring expert instructors that provide a deeper understanding of the steps involved in using AI algorithms in medical imaging.
Content Areas (Codes): The following Content Areas will be printed on the certificate for this course:
Learning Objectives:
- Prepare radiologists, physicists, data scientists, and clinical researchers to evaluate the AI models fairness across various populations
- Interpret the AI lifecycle beginning with training and test data curation to FDA approval
- Provide participants with a deep understanding of the pitfalls of dataset curation, pre-processing, and annotation when initiating AI for clinical use
- Recognize the impact of regulatory environment, the clinical AI marketplace, and ethical considerations on the delivery of AI in healthcare
Start Date: 01/18/2023
Online Expiration Date: 06/20/2026
Price:
Non-Member/Basic Member Rate: $1150.00
Standard Member/Full Access Member Rate: $950.00
Member in Training Rate: $750.00
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
Learning Objectives:
- Assess the purpose and complexities involved when developing machine learning models and applications
- Demonstrate different approaches to generating and working on AI issues
- Identify ethical considerations involved in data sharing of patient privacy and consent
This Other Activity (blended enduring material and simulation) is estimated to take 2 hour to complete. This activity has not been designated for continuing medical education credit.
Faculty:
- Kathy Andriole, PhD
- Imon Banerjee, PhD
- Judy Gichoya, MBChB
- Marta Heilbrun, MD
- Curtis P. Langlotz, MD, PhD
- Matthew Lungren, MD, MPH
- Ian Pan, MD
- Nabile Safdar, MD
Learning Objectives:
- Assess the purpose and complexities involved when developing machine learning models and applications
- Demonstrate different approaches to generating and working on AI issues
- Identify ethical considerations involved in data sharing of patient privacy and consent
This Other Activity (blended enduring material and simulation) is estimated to take 2 hour to complete. This activity has not been designated for continuing medical education credit.
Faculty:
- Imon Banerjee, PhD
- Leo Anthony Celi, MD
- Marta Heilbrun, MD
- Panagiotis Korfiatis, PhD
- Elizabeth Krupinski, PhD
- Andras Lasso, PhD
- Kevin O'Donnell, MASc.
- Nabile Safdar, MD
- George Shih, MD, MS
- Ronald M. Summers, MD, PhD
- Hari Trivedi, MD
This Other Activity (blended enduring material and simulation) is estimated to take 1 hour and 30 minutes to complete.
Faculty:
- Imon Banerjee, PhD
- Marta Heilbrun, MD
- Andras Lasso, PhD
- Nabile Safdar, MD
Learning Objectives:
- Interpret conceptual differences and advantages between a vision transformer (ViT) and a convolutional neural network (CNN)
- Assess the strengths and weaknesses of convoluational neural networks (CNN) and different CCN architectures such as AlexNet, VGG, ResNet, and DenseNet
- Implement the fundamentals of recurrent neural network (RNN) and recent use cases in radiology specific tasks
This Other Activity (blended enduring material and simulation) is estimated to take 3 hours to complete.
Faculty:
- Imon Banerjee, PhD
- Marta Heilbrun, MD
- Bhavik Patel, MD, MBA
- Jean-Baptiste Poline, PhD
- Luciano M. Prevedello, MD, MPH
- Daniel L Rubin, MD, MS
- Nabile Safdar, MD
- Rakesh Shiradkar, PhD
- Paul Yi, MD
Learning Objectives:
- Identify the steps to effectively evaluate an AI model in your clinical environment
- Implement an evaluation framework for pre-purchasing AI tools
- Assess the various stages in evaluation and deployment of AI and identify the best approach for its intended use
This Other Activity (blended enduring material and simulation) is estimated to take 2 hours and 30 minutes to complete.
Faculty:
- Bhanushree Bahl, BDS, MBA
- Imon Banerjee, PhD
- Tessa Cook, MD, PhD
- Melissa Davis, MD, MBA
- Marta Heilbrun, MD
- Jayashree Kalpathy-Cramer, PhD
- Nina Kottler, MD, MS
- Vidur Mahajan, MD, MBA, MBBS
- Nabile Safdar, MD
Learning Objectives:
- Identify essential FDA regulatory processes for AI software devices
- Interpret new regulatory challenges of the FDA that come with effectively overseeing the realm of AI/ML-enabled medical devices
- Differentiate available resources for evaluating FDA cleared AI algorithms of clinical use
- Assess payment structure of CMS and various financial returns on investment through the utilization of AI software
This Other Activity (blended enduring material and simulation) is estimated to take 1 hour and 30 minutes to complete.
Faculty:
- Bibb Allen, MD, FACR
- Imon Banerjee, PhD
- Melissa M. Chen, MD
- Hugh Harvey, MBBS, BSc (Hons), FRCR, MD (Res), FBIR
- Marta Heilbrun, MD
- Shinjini Kundu, MD, PhD
- Nabile Safdar, MD
- Hari Trivedi, MD
Learning Objectives:
- Interpret machine learning uses or abuses for each of the four principles of bioethics
- Illustrate common biases that may influence radiologists' use of AI prompts that impact decision during image interpretation
- Demonstrate concepts of fairness in ML and the importance of critically evaluating bias in AI algorithms prior to deployment in healthcare
This Other Activity (blended enduring material and simulation) is estimated to take 1 hour and 30 minutes to complete.
Faculty:
- Patricia Balthazar, MD, CIIP
- Imon Banerjee, PhD
- John Banja, PhD
- Raym Geis, MD
- Judy Gichoya, MBChB
- Marta Heilbrun, MD
- Marc Kohli, MD
- Nina Kottler, MD, MS
- Elizabeth Krupinski, PhD
- Bhavik Patel, MD, MBA
- Nabile Safdar, MD