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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
