Stanford University

AI in Healthcare Specialization

Stanford University

AI in Healthcare Specialization

Matthew Lungren
Serena Yeung
Mildred Cho

Instructors: Matthew Lungren

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Get in-depth knowledge of a subject

from 2,526 reviews of courses in this program

Beginner level
No prior experience required
4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject

from 2,526 reviews of courses in this program

Beginner level
No prior experience required
4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Identify problems healthcare providers face that machine learning can solve

  • Analyze how AI affects patient care safety, quality, and research

  • Relate AI to the science, practice, and business of medicine

  • Apply the building blocks of AI to help you innovate and understand emerging technologies

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Taught in English

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Specialization - 5 course series

Introduction to Healthcare

Introduction to Healthcare

Course 1, 12 hours

What you'll learn

  • The major challenges of the U.S.healthcare system

  • Issues you may encounter in efforts to improve healthcare delivery and the healthcare system

  • Who the key stakeholders are in the U.S. healthcare system

Skills you'll gain

Category: Health Policy
Category: Health Systems
Category: Medicaid
Category: Medicare
Category: Drug Development
Category: Care Management
Category: Pharmaceuticals
Category: Health Care Procedure and Regulation
Category: Value-Based Care
Category: Healthcare Ethics
Category: Managed Care
Category: Hospital Medicine
Category: Medical Management
Category: Health Care
Category: Medical Billing
Category: Hospital Admissions
Category: Health Care Administration
Category: Ethical Standards And Conduct
Category: Healthcare Industry Knowledge
Introduction to Clinical Data

Introduction to Clinical Data

Course 2, 12 hours

What you'll learn

  • How to apply a framework for medical data mining

  • Ethical use of data in healthcare decisions

  • How to make use of data that may be inaccurate in systematic ways

  • What makes a good research question and how to construct a data mining workflow answer it

Skills you'll gain

Category: Electronic Medical Record
Category: Data Mining
Category: Feature Engineering
Category: Clinical Data Management
Category: Healthcare Ethics
Category: Data Ethics
Category: Data Collection
Category: Data Preprocessing
Category: Medical Records
Category: Clinical Research
Category: Responsible AI
Category: Clinical Informatics
Category: Health Disparities
Category: Unstructured Data
Category: Health Informatics
Category: Data Processing
Category: Medical Imaging
Category: Text Mining
Category: Data Transformation
Fundamentals of Machine Learning for Healthcare

Fundamentals of Machine Learning for Healthcare

Course 3, 14 hours

What you'll learn

  • Define important relationships between the fields of machine learning, biostatistics, and traditional computer programming.

  • Learn about advanced neural network architectures for tasks ranging from text classification to object detection and segmentation.

  • Learn important approaches for leveraging data to train, validate, and test machine learning models.

  • Understand how dynamic medical practice and discontinuous timelines impact clinical machine learning application development and deployment.

Skills you'll gain

Category: Machine Learning Algorithms
Category: Machine Learning
Category: Model Evaluation
Category: Supervised Learning
Category: Machine Learning Methods
Category: Healthcare Ethics
Category: Data Ethics
Category: Medical Science and Research
Category: Applied Machine Learning
Category: Responsible AI
Category: Generative Model Architectures
Category: Statistical Machine Learning
Category: Reinforcement Learning
Category: Artificial Neural Networks
Category: Deep Learning
Category: Healthcare Industry Knowledge
Category: Health Policy
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Health Informatics
Category: Model Training
Evaluations of AI Applications in Healthcare

Evaluations of AI Applications in Healthcare

Course 4, 11 hours

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

Skills you'll gain

Category: Responsible AI
Category: Healthcare Ethics
Category: AI Integrations
Category: Clinical Research Ethics
Category: Predictive Modeling
Category: Healthcare Industry Knowledge
Category: Application Deployment
Category: Clinical Assessment
Category: Continuous Monitoring
Category: Regulatory Compliance
Category: Data Ethics
Category: Health Technology
Category: Health Disparities
Category: Health Equity
Category: Decision Support Systems
AI in Healthcare Capstone

AI in Healthcare Capstone

Course 5, 11 hours

What you'll learn

Skills you'll gain

Category: Model Evaluation
Category: Applied Machine Learning
Category: Model Optimization
Category: Health Care Procedure and Regulation
Category: Responsible AI
Category: Health Informatics
Category: Performance Tuning
Category: Clinical Data Management
Category: Artificial Intelligence
Category: Fine-tuning
Category: Machine Learning
Category: Data Ethics
Category: Model Deployment
Category: Data Collection
Category: Health Information Management
Category: Patient-centered Care
Category: Machine Learning Software
Category: Model Training
Category: Healthcare Ethics
Category: Artificial Intelligence and Machine Learning (AI/ML)

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Instructors

Matthew Lungren
Stanford University
2 Courses46,322 learners
Serena Yeung
Stanford University
2 Courses46,322 learners

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