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Course Outline
Introduction to Artificial Intelligence in Healthcare
- Overview of artificial intelligence (AI) and machine learning applications in medicine
- Historical development of AI in the healthcare sector
- Key opportunities and challenges associated with AI adoption for government and private healthcare systems
Healthcare Data and Artificial Intelligence
- Types of healthcare data: structured and unstructured information
- Data privacy and security regulations, including HIPAA and GDPR, for government and private entities
- Ethical considerations in AI-driven healthcare practices
Machine Learning Fundamentals for Healthcare Applications
- Supervised versus unsupervised learning methodologies
- Feature engineering and data preprocessing techniques for medical datasets
- Evaluating AI models in healthcare applications to ensure accuracy and reliability
Artificial Intelligence Applications in Patient Care
- Use of AI in medical imaging and diagnostics for enhanced precision
- Predictive analytics for improved patient outcomes and risk management
- Personalized medicine and treatment recommendations using AI algorithms
Artificial Intelligence for Hospital and Clinical Operations
- Automating administrative tasks with AI to enhance efficiency for government and private healthcare providers
- AI-driven decision support systems for better clinical outcomes
- Optimizing hospital resource management through AI applications
Ethics, Bias, and Governance of Artificial Intelligence in Healthcare
- Understanding bias in medical AI models and its implications for patient care
- Regulatory and compliance considerations for government and private healthcare organizations
- Ensuring transparency and accountability in AI systems to maintain public trust
Capstone Project: AI-Driven Patient Data Analysis
- Exploring a comprehensive healthcare dataset for government or private use
- Building and evaluating an AI model for medical predictions to enhance patient care
- Interpreting model outputs and refining accuracy through iterative improvements
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts for government applications.
- Proficiency in Python programming.
- Familiarity with healthcare data or clinical workflows is advantageous.
Audience
- Healthcare professionals interested in the application of artificial intelligence for government and healthcare sectors.
- Data scientists and AI engineers working within the healthcare industry.
- Technology leaders and decision-makers in the medical field who are focused on advancing public sector workflows and governance through AI solutions.
21 Hours
