Introduction to Artificial Intelligence and Machine Learning in Healthcare

Overview

Gain insights into artificial intelligence and machine learning fundamentals through a healthcare lens, exploring key concepts and applications in diagnostic cardiology.

Syllabus

• Course Introduction
Overview of AI and ML
Significance of AI in Healthcare
Course Objectives and Structure
• Fundamentals of Artificial Intelligence
Definition and History of AI
Key Components: Algorithms, Data, and Computing Power
Types of AI: Narrow vs. General AI
• Introduction to Machine Learning
Definition and Core Concepts
Types of ML: Supervised, Unsupervised, and Reinforcement Learning
ML Workflow: Data Collection, Model Training, and Evaluation
• Machine Learning in Healthcare
Overview of Healthcare Data Types: EHRs, Imaging, Genomics
Challenges of ML in Healthcare: Privacy, Bias, and Ethical Concerns
Opportunities for ML: Personalized Medicine and Predictive Analytics
• Key ML Algorithms in Healthcare
Decision Trees and Random Forests
Neural Networks and Deep Learning
Support Vector Machines and Ensemble Methods
• AI in Diagnostic Cardiology
Applications of AI in Cardiac Imaging
AI for Predicting Cardiovascular Risk
Case Studies: AI Success Stories in Cardiology
• Practical Implementation of AI in Healthcare
Overview of Tools and Frameworks: TensorFlow, PyTorch
Setting up a Machine Learning Pipeline
Integrating AI Solutions in Clinical Settings
• Ethical and Regulatory Considerations
AI in Healthcare Regulations
Addressing Ethical Issues: Fairness, Accountability, and Transparency
Future Directions and Emerging Trends
• Course Conclusion
Recap of Key Concepts
Discussion of Future Trends in AI and ML in Healthcare
Resources for Further Learning
• Assessments and Assignments
Quizzes and Interactive Labs
Final Project: AI Application in Diagnostic Cardiology
Feedback and Course Evaluation

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Subjects

Data Science