AI Foundations and Applications for Emerging Digital Healthcare Leaders
Self-Paced Online
8/1/2024 - 12/31/2025
$250
1 - 2 hours
AI Foundations and Applications for Emerging Digital Healthcare Leaders is designed for healthcare professionals seeking to become critical consumers and responsible users of technologies referred to as "AI." Learners can complete this introductory course in just one to two hours, with options to explore topics of interest in greater depth.
Summary
AI Foundations and Applications for Emerging Digital Healthcare Leaders is a self-paced, online course for healthcare professionals seeking greater understandings of AI technologies in their professional contexts. Through video lectures, animated videos, readings, and interactive materials, internationally recognized Mayo Clinic faculty will review foundational terminology and concepts, provide example use cases of AI technologies in healthcare, and engage in critical questions we ask as critical consumers and responsible users of artificial intelligence in healthcare.
Upon successful completion of the program, learners earn a certificate of completion.
Access to this online course is available from the date of purchase until it expires on 12/31/2025.
Four modules organize the content into short lessons that busy professionals can complete:
- Artificial Intelligence and Machine Learning Fundamentals: How do artificial intelligence and machine learning technologies work and how are they used in healthcare?
- Large Language Models Fundamentals: What are important considerations for using large language models in the context of healthcare?
- Computational Thinking, Healthcare, and Machine Learning: What are the various types of healthcare data used in these technologies? How do various model types use different types of data?
- Evaluating Machine Learning Model Accuracy: What questions can we ask vendors and subject matter experts to critically evaluate a model's performance?
After completing the course, learners will be able to:
- Define artificial intelligence and machine learning technologies;
- Identify example use cases of these technologies in healthcare; and
- Critically evaluate if the accuracy of a machine learning model is adequate for an intended use case.
Technical Requirements
- Computer with internet access
- Speaker or headset
- Web browser
This introductory course is designed for healthcare administrators and clinicians seeking to learn more about varied technologies referred to as "AI" and how such technologies are used to advance healthcare. The knowledge gained here will lay the foundation for those seeking to become critical consumers and responsible users of such technologies in their professional contexts.
 |
Santiago Romero-Brufau, M.D., Ph.D., Course DirectorSantiago Romero-Brufau, M.D., Ph.D. is the Director of AI and Systems Engineering in the Department of ENT at Mayo Clinic, where he is Assistant Professor of Healthcare Systems Engineering and ENT. His work focuses on the development and implementation of AI and machine-learning solutions into clinical practice. He is also an Adjunct Assistant Professor and a member of the Executive Committee for the Master's in Health Data Science at the Harvard T.H. Chan School of Public Health, where he teaches deep learning, and how to implement machine-learning models into the clinical workflow. |
 |
Jason Greenwood, M.D., M.S.Jason Greenwood, M.D., M.S. is a Senior Associate Consultant in the Mayo Clinic Department of Family Medicine in Rochester, MN, and an Assistant Professor at the Mayo Clinic College of Medicine and Science who is board-certified in Clinical Informatics and has industry experience in computer engineering. He is committed to driving primary care adoption and integration of technology through the development and delivery of new tooling, including AI/ML models. His work focuses on developing and promoting innovative tools and designs that improve primary care providers' workflow and efficiency and decrease chances of burnout with the goal of saving providers hundreds of hours of time annually in chart review, in basket work and documentation while simultaneously improving patient outcomes. |
 |
Lauren Rost, Ph.D.Lauren Rost, Ph.D. is the Enterprise AI Translation Advisory Board Chair and a Senior AI/ML Engineer within the Center for Digital Health. She provides expertise in AI feasibility and design, informatics, metadata management, and AI education to enable and promote AI best practices across the enterprise. She is a leader in the healthcare AI space, frequently presenting on AI best practices and governance at national conferences. |
