Course Outline

Introduction to AI in Healthcare

  • Overview of AI and machine learning in medicine
  • Historical development of AI in healthcare
  • Key opportunities and challenges in AI adoption

Healthcare Data and AI

  • Types of healthcare data: structured and unstructured
  • Data privacy and security regulations (HIPAA, GDPR)
  • Ethical considerations in AI-driven healthcare

Machine Learning Fundamentals for Healthcare

  • Supervised vs. unsupervised learning
  • Feature engineering and data preprocessing for medical datasets
  • Evaluating AI models in healthcare applications

AI Applications in Patient Care

  • AI in medical imaging and diagnostics
  • Predictive analytics for patient outcomes
  • Personalized medicine and treatment recommendations

AI for Hospital and Clinical Operations

  • Automating administrative tasks with AI
  • AI-driven decision support systems
  • Optimizing hospital resource management

Ethics, Bias, and AI Governance in Healthcare

  • Understanding bias in medical AI models
  • Regulatory and compliance considerations
  • Ensuring transparency and accountability in AI systems

Capstone Project: AI-Driven Patient Data Analysis

  • Exploring a healthcare dataset
  • Building and evaluating an AI model for medical predictions
  • Interpreting model outputs and improving accuracy

Summary and Next Steps

Requirements

  • Basic understanding of machine learning concepts
  • Experience with Python programming
  • Familiarity with healthcare data or clinical workflows is beneficial

Audience

  • Healthcare professionals interested in AI applications
  • Data scientists and AI engineers working in healthcare
  • Technology leaders and decision-makers in the medical field
 21 Hours

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