AI in Healthcare Capstone
Apply AI and machine learning concepts to real healthcare scenarios through a comprehensive patient case study.
Apply AI and machine learning concepts to real healthcare scenarios through a comprehensive patient case study.
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.
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What you'll learn
Build healthcare risk-stratification models
Analyze patient data across multiple modalities
Evaluate AI model performance in clinical contexts
Understand healthcare AI regulatory requirements
Implement ethical AI solutions in healthcare
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
9 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This capstone project follows a patient's healthcare journey through the lens of data and AI applications. Students work with a unique de-identified dataset combining EHR and image data to build risk-stratification models. The course covers the complete AI implementation cycle from data collection through model deployment, while addressing regulatory and ethical considerations in healthcare AI applications.
Getting Started, Phase 1: Data Collection
Module 1 · 2 Hours to complete
Phase 2: Model Training Part 1
Module 2 · 2 Hours to complete
Phase 3: Model Training Part 2
Module 3 · 2 Hours to complete
Phase 4: Model Evaluation
Module 4 · 2 Hours to complete
Phase 5: Model Deployment and Regulation, Wrap Up
Module 5 · 1 Hours to complete
Fee Structure
Instructors
Leading Health Economist and Professor at Stanford University
Laurence Baker, Ph.D., is the Bing Professor of Human Biology and a distinguished member of the Health Policy group at Stanford University, where he applies his expertise in economic and statistical analysis to tackle pressing challenges within the healthcare system. As an accomplished health economist, Professor Baker teaches both undergraduate and graduate courses and has published extensively on various healthcare financing and system issues. His research delves into the impacts of financial incentives, technological advancements in medicine, competition in healthcare markets, and the dynamics of managed care and insurance plans. In addition to his primary role at Stanford, he is a Professor of Economics (by courtesy), a Senior Fellow at the Stanford Institute for Economic Policy Research, and a Research Associate at the National Bureau of Economic Research. A former Chair of the Department of Health Research and Policy at Stanford, Professor Baker has received numerous accolades, including the ASHE Medal from the American Society of Health Economists and the Alice S. Hersh Young Investigator Award from AcademyHealth. He holds a Ph.D. in Economics from Princeton University and a B.A. in Economics and Mathematics from Calvin College.
Academic Director, AI in Healthcare Specialization; Associate Professor
Dr. Nigam Shah is an esteemed Associate Professor of Medicine specializing in Biomedical Informatics at Stanford University, where he also serves as the Associate Chief Information Officer for Data Science at Stanford Health Care. His research is centered on integrating machine learning with medical ontologies to enhance the learning health system, enabling better healthcare delivery and clinical decision-making. Dr. Shah has made significant contributions to the field, including being elected to the American College of Medical Informatics in 2015 and inducted into the American Society for Clinical Investigation in 2016. He holds an MBBS from Baroda Medical College, India, and a PhD from Penn State University, complemented by postdoctoral training at Stanford.In addition to his academic roles, Dr. Shah leads various initiatives in artificial intelligence and data science aimed at improving patient care through innovative technologies. He has authored over 200 scientific publications and holds eight patents related to his research. His expertise extends to analyzing diverse health data types, including electronic health records and wearable device data, to generate insights and predictive models that address pressing clinical questions. Through his work, Dr. Shah aims to bring AI into clinical practice safely and effectively, ensuring that these advancements are ethically applied in healthcare settings.
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