AI in Healthcare Specialization

After Completing AI in Healthcare Specialization, you will be able to learn…

  • Fundamentals of the U.S. healthcare system
  • Major challenges of the system
  • Key stakeholders are in the U.S. healthcare system
  • Key Challenges in healthcare system
  • Issues you may encounter in efforts to improve healthcare delivery and the healthcare system
  • Capitation Payment Systems
  • Operations and Characteristics of Hospitals
  • Hospital Payment Methods
  • Risk and Incentives in Hospital Payment
  • Intermediaries and the Broad Challenges Facing Health Care Systems
  • Health care products, approvals, and prescription drugs approval processes
  • AI applications in delivery of health care services and ethical issues
  • AI and incentives in health care delivery and payment structures
  • How to construct Data mining workflow
  • Common data types in Healthcare
  • Strengths and weaknesses of observational data
  • Time series and non-time series data
  • Timelines, timescales and representations of time
  • Timing of exposures and outcomes
  • Different ways to put data in bins
  • How to create features from structured sources
  • Using features and the presence of features
  • How to deal with missing values & too many features
  • What are signals and Why signals are important?
  • Major issues with using signals
  • Challenges in electronic phenotyping
  • Ethical Issues in Data sources for AI
  • 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
  • Supervised Machine Learning Approaches
  • Traditional Supervised Machine Learning Approaches
  • Deep Learning and Neural Networks
  • Representing Unstructured Image and Text Data
  • Natural Language Processing and Recurrent Neural Networks
  • Convolutional Neural Networks
  • Advanced Neural Network Architectures
  • Statistical Approaches to Model Evaluation
  • Common Clinical Machine Learning Challenges
  • Medical Data Challenges in Machine Learning
  • Building Multidiciplinary Teams for Clinical Machine Learning
  • 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
  • Standard evaluation metrics do and do not provide
  • Why AI is needed in Healthcare and Growth of AI in Healthcare
  • Care Integration, Clinical Integration & Technical Integration, Considerations
  • Types of Bias (Historical Bias, Representation Bias, Measurement Bias, Aggregation Bias, Evaluation Bias, Deployment Bias)
  • Identifying & Mitigating Conflicts of Interest

AI in Healthcare Specialization includes 5 Courses they are

  1. Introduction to Healthcare
  2. Introduction to Clinical Data
  3. Fundamentals of Machine Learning for Healthcare
  4. Evaluations of AI Applications in Healthcare
  5. AI in Healthcare Capstone

Course Instructors Nigam Shah, Mildred Cho, Laurence Baker, Steven Bagley, David Magnus, Matthew Lungren, Serena Yeung, Tina Hernandez-Boussard & Offered by Stanford University from Coursera

Course Duration: Approximately 9 months to complete (Suggested pace of 2 hours/week)

Course is for Beginner Level

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