Chest Certificate Curriculum
The Chest Certificate course is now offered with improved pricing to better fit your budget. Visit the pricing page to explore enrollment details.
AI is rapidly transforming the way chest radiologists handle the high volume of cases by optimizing workflow integration and automation. The curriculum in the Chest Certificate course will enable you to:
- Gain hands-on experience and develop practical skills through case-based learning
- Responsibly integrate AI tools to optimize your workflow and automate tasks
- Bring structure and speed to your reporting to better handle high imaging volumes
- Flag critical findings faster
- Assess model performance
- Explore insights from the latest research
Meet the demands of critical cases in chest imaging and distinguish yourself as a leader in imaging innovation. Enroll today to earn your Chest Certificate!
Course Modules
Each of the six case-based modules allow 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.
All six modules are available upon enrollment.
In module two, you will learn technical and clinical evaluations, including:
- Determining ROI
- Developing collaborative opportunities
- Workflow integration
- Use of IHE standards and model effectiveness
You will also learn how to review the market and where to find FDA-cleared and other agency-cleared information.
In module three, you will:
- Explore the role of CADt in chest imaging and its impact on radiology workflows
- Learn how CADt can expedite care by flagging urgent findings and enabling faster intervention
- Discover how CADt streamlines workflows and boosts collaboration, while assessing AI value from multiple stakeholder perspectives
In module four, you will:
- Explore AI’s role in streamlining care coordination from workflows and efficiency to patient outcomes
- Gain tools and strategies that support both critical and non-critical follow-up pathways
- Learn to strengthen care-team collaboration and evaluate the value proposition of AI-driven solutions from multiple institutional perspectives
In module five, you will:
- Examine how generative AI, natural language processing (NLP), and large language models (LLMs) are transforming radiology reporting workflows
- Explore how these technologies support automation of report generation, template and hanging protocol selection, metadata normalization, billing code assignment, and patient communication through practical demonstrations and applied use cases
- Consider implementation strategies and post-deployment follow-up to ensure sustained value and alignment with institutional goals
Module content emphasizes AI’s role as an enabling technology that enhances reporting accuracy, reduces cognitive burden and improves operational efficiency.
- Describe the categories of knowledge radiologists need before implementing AI in clinical practice, including general AI concepts, use cases, product-specific details, and institution-specific applications.
- Explain how overreliance on AI can affect human interpretation and diagnostic accuracy.
- Compare automation in radiology with similar trends in other fields to identify shared challenges and lessons.
- Analyze integration strategies that help reduce bias in AI-assisted workflows.
- Summarize the foundational principles, capabilities, and limitations of AI drift monitoring tools.
- Identify and evaluate reliable resources for discovering AI companies and products tailored to institutional needs.
“AI can dramatically shorten turnaround time for identifying critical findings in chest imaging.”
— Radiology Business