Evaluations of AI Applications in Healthcare
Stanford University
With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions. In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation
About this Course
With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions. In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation
What You'll Learn
- Principles and practical considerations for integrating AI into clinical workflows
- Best practices of AI applications to promote fair and equitable healthcare solutions
- Challenges of regulation of AI applications and which components of a model can be regulated
- What standard evaluation metrics do and do not provide
Instructors
Tina Hernandez-Boussard
Medicine (Biomedical Informatics), of Biomedical Data Science and of Surgery
Mildred Cho
Department of Pediatrics Division of Medical Genetics