Advanced Certificate curriculum

The RSNA Imaging AI Certificate Program offers a pathway of certificate courses, providing you with the ability to harness the AI knowledge critical to meeting the challenges in the medical imaging field.

The curriculum described on this page is for the program’s Advanced Certificate course. It’s the next step on the pathway to help radiologists confidently use AI tools and is designed to provide a deeper understanding of the steps involved in using AI algorithms in medical imaging.

Pricing & enrollment

Outcomes and learning objectives

Upon completion of the six-module curriculum, enrollees will earn the Advanced Certificate, recognizing their ability to evaluate the fairness of AI models across populations, interpret the AI lifecycle, examine the pitfalls of using AI in a clinical setting and recognize the impact of the regulatory environment affecting the use of AI in medical imaging.

Advanced Certificate course learning objectives:

  • Prepare radiologists, physicists, data scientists and clinical researchers to evaluate the AI model’s 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 health care.

Course modules

Each case-based module allows you to learn at your own pace through a series of pre-recorded videos and a variety of hands-on activities that build on concepts established in the previous modules.

Modules for the Advanced Certificate course will be released on the third Wednesday of the month from Jan. 18 through June.

Module 1: Introduction to AI

You will assess the purpose and complexities involved when developing machine learning models and applications. This module will demonstrate different approaches to generating models and resolving AI issues. You will also learn to identify the ethical considerations involved in data sharing including patient privacy and consent.

Module 2: Dataset Curation, Image Preprocessing, and Annotation

You will identify key elements of image annotation tools and the infrastructure required and interpret the standard formats most suited to imaging annotations. This module will help you recognize pertinent factors in DICOM metadata, pixel level de-identification and existing software solutions. You will also assess the benefits and limitations of using human readers to annotate medical images and computer-based analyses.

Module 3: Model Building
In Module 3, you will interpret conceptual differences and advantages between a vision transformer and a convolutional neural network (CNN). You will learn to assess the strengths and weaknesses of CNN and different architectures such as AlexNet, VGG, ResNet, and DenseNet. You will also learn to implement the fundamentals of recurrent neural network.
Module 4: Model Evaluation
In Module 4, you will learn the purpose, standard metrics and critical steps needed for AI model evaluation. You will assess the differences in AI evaluation tools and how to train models for distributed and federated learning to ensure privacy. You also will have the opportunity to test your new skills with hands-on exercises.

"I teach informatics and will use this knowledge to enhance my course content."

— Advanced Certificate Enrollee, 2023

This activity has not been designated for continuing medical education credit.

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