COStanford University · on Coursera
AI in Healthcare Specialization
Intermediate 120 hours English Specialization Certificate Certificate
About this course
This Stanford specialization covers the unique challenges of healthcare AI: clinical datasets, diagnostic models for medical imaging, electronic health record interpretation, and rigorous model evaluation in clinical contexts where errors have serious consequences.
The curriculum addresses regulatory considerations, algorithmic bias in healthcare, and the challenges of deploying AI in clinical workflows — the most comprehensive academic healthcare AI curriculum available on a MOOC platform.
What you'll learn
Apply ML to structured clinical data including EHR and claims data
Build diagnostic models for medical imaging including chest X-ray
Evaluate clinical AI models with appropriate medical performance metrics
Understand FDA regulation and clinical validation for AI systems
Identify and mitigate algorithmic bias in healthcare ML
This course includes
120h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
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Instructor
SY
Serena Yeung / Andrew Ng
Coursera instructor
95K+ learners4 courses4.7 instructor rating
Taught by Stanford Medicine faculty including Andrew Ng and physician-researchers with expertise in clinical AI deployment.
Requirements
- Basic Python and ML fundamentals; clinical knowledge helpful but not required
Who this course is for
- Healthcare professionals learning to evaluate and work with AI tools
- ML engineers entering healthcare or medical device industries
- Researchers applying AI to clinical or biomedical problems
About this provider
CO
Coursera
University-backed online learning platform. 142M learners, 7,000+ courses from 325+ institutions.
Frequently asked questions
No — clinical concepts are explained as needed. ML background is more important than medical background.
Partially — the clinical data and imaging modules apply; drug discovery AI is a separate domain.