Foundational 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 Foundational Certificate course. It’s the first step on the pathway to more advanced and specialty-specific certificate courses and is designed to help radiologists stay up-to-date with the latest AI technology and utilization.
Foundational Certificate course outcomes and learning objectives
Upon completion of the six-module curriculum, students will earn the Foundational Certificate, recognizing their understanding of AI-based tools for medical imaging and their ability to safely evaluate, deploy, monitor and use AI algorithms within their practice.
- Prepare radiologists, physicists, data scientists and clinical researchers to safely evaluate, implement, use and monitor performance of AI-based tools for medical imaging.
- Deliver coordinated and comprehensive AI education that prepares radiologists to evaluate and use AI algorithms for clinical practice.
- Demonstrate the steps and process of AI algorithm development.
- Provide participants the ability to safely evaluate, deploy, monitor and use AI algorithms within their practice.
Foundational Certificate course modules
Each of the six modules allows you to learn at your own pace through a series of short, pre-recorded videos and a variety of hands-on activities that build on concepts established in the previous modules.
Module 1 — Introduction to AI in Radiology
In Module 1, you will review the history of ImageNet, machine learning definitions, origins of the field, early uses of AI in radiology, common workflow problems that can be solved using AI and insights from RSNA’s AI Challenges.
Module 2 — Data Curation
In Module 2, you will review key elements of data curation, focus on the importance of matching the data source with the intended use of the AI model, obtain an understanding of various methods for data de-identification and preprocessing steps, and apply your learning using hands-on exercises.
Module 3 — Data Annotation and Model Building
In Module 3, you will review key elements of data annotation, examine machine learning models and understand key concepts for training and validating an AI model. You will be able to identify potential barriers of model building, determine the metrics used to measure performance, and apply your new skills via hands-on exercises.
Module 4 — Model Evaluation
In Module 4, you will define the stages of AI model evaluation for medical imaging. This module will help you interpret the complex nature of AI evaluation in clinical practice and identify the tools necessary to help analyze AI systems. You will also have the opportunity to apply skills learned using hands-on exercises.
Module 5 — AI Ethics
In Module 5, you will review the concepts and framework around imaging AI ethics including data sharing and analysis, AI algorithms and clinical implementation. This module will help you assess the risks of AI data collection and formulate strategies to integrate responsible machine learning to proactively advance health equity.
Module 6 — Clinical Implementation
In Module 6, you will explore the black box problem and role of explainable AI including transferability, over-reliance and human factors. This module will help you assess if your practice is ready for AI, vendor management and AI standards including reporting and workflow. Finally, this module will explore real world AI uses as well as AI implementation internationally.
“The excellent variety of real-world examples and uses of AI really increased my interest in becoming an early AI adopter and champion at my facility.”
—Foundational Course Alumnus, August 2022
Accreditation and Designation Statements
The Radiological Society of North America (RSNA) is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
This activity has been approved for AMA PRA Category 1 Credits™.