Evaluations of AI Applications in Healthcare
Learn to evaluate AI healthcare solutions, from deployment principles to regulatory frameworks, with focus on fairness and clinical integration.
Learn to evaluate AI healthcare solutions, from deployment principles to regulatory frameworks, with focus on fairness and clinical integration.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full AI in Healthcare Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.6
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English
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What you'll learn
Evaluate AI integration in clinical workflows
Identify and mitigate bias in healthcare AI applications
Navigate regulatory frameworks for healthcare AI
Assess fairness metrics in AI healthcare solutions
Implement best practices for ethical AI deployment
Skills you'll gain
This course includes:
3.5 Hours PreRecorded video
22 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 8 modules in this course
This comprehensive course explores the evaluation and deployment of AI applications in healthcare settings. Students learn about integrating AI into clinical workflows, addressing bias and fairness concerns, and navigating regulatory requirements. The curriculum covers practical considerations for implementation, ethical practices, and downstream effects of AI healthcare solutions, with emphasis on stakeholder involvement and continuous monitoring.
AI in Healthcare
Module 1 · 1 Hours to complete
Evaluations of AI in Healthcare
Module 2 · 1 Hours to complete
AI Deployment
Module 3 · 2 Hours to complete
Downstream Evaluations of AI in Healthcare: Bias and Fairness
Module 4 · 1 Hours to complete
The Regulatory Environment for AI in Healthcare
Module 5 · 1 Hours to complete
Best Ethical Practices for AI in Health Care
Module 6 · 30 Minutes to complete
AI and Medicine
Module 7 · 41 Minutes to complete
Course Wrap Up
Module 8 · 1 Hours to complete
Fee Structure
Instructors
Leader in Biomedical Informatics and Health Services Research
Dr. Tina Hernandez-Boussard is an Associate Professor of Medicine (Biomedical Informatics), Biomedical Data Sciences, and Surgery at Stanford University. With a robust background in bioinformatics and health services research, her work focuses on applying innovative methods to large clinical datasets to enhance healthcare delivery. Over the past decade, Dr. Hernandez-Boussard has utilized electronic medical records and extensive digital data to monitor, measure, and predict healthcare outcomes through advanced machine learning and deep learning techniques. Her research infrastructure captures diverse data sources, transforming them into actionable knowledge that integrates seamlessly into clinical workflows. Dr. Hernandez-Boussard also serves as the Chairperson of the National Advisory Council for the Agency for Healthcare Research and Quality and is the Director of Faculty Development in Biomedical Informatics at Stanford. She holds a PhD in Computational Biology from Lyon University, a Master’s in Public Health from Yale University, and a Master’s in Health Services Research from Stanford University. Through her pioneering research and leadership roles, she is dedicated to advancing equitable healthcare practices and improving patient outcomes across diverse populations.
Influential Scholar in Medical Genetics and Bioethics at Stanford University
Dr. Mildred Cho is a Professor in the Division of Medical Genetics within the Department of Pediatrics and the Division of Primary Care and Population Health at Stanford University. She also serves as the Associate Director of the Stanford Center for Biomedical Ethics and directs the Center for Integration of Research on Genetics and Ethics. Dr. Cho earned her B.S. in Biology from the Massachusetts Institute of Technology in 1984 and her Ph.D. in Pharmacology from Stanford University in 1992. Her research primarily focuses on the ethical and social implications of genetic research, including applications in precision medicine, gene therapy, and synthetic biology. Recently, she has explored how data science, artificial intelligence, and mobile technologies can impact genomic and health data, emphasizing the importance of integrating ethical considerations into these advancements. Dr. Cho is a member of the National Institutes of Health's Novel and Exceptional Technology and Research Advisory Committee, reflecting her commitment to shaping responsible practices in biomedical research. Through her extensive work, she significantly contributes to understanding the intersection of genetics, ethics, and technology in healthcare.
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4.6 course rating
234 ratings
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