Foundations of AI in Healthcare
Day 1: Introduction to AI and its Role in Healthcare Transformation
Evolution of AI technologies in healthcare
Key terminologies: Machine learning, deep learning, and natural language processing
Industry trends driving AI adoption
Ethical considerations in AI implementation
Day 2: Data Management and Governance
Principles of data collection, storage, and preprocessing
Ensuring data quality, accuracy, and integrity
Regulatory frameworks: HIPAA, GDPR, and their implications
Building a robust data governance strategy
Day 3: Basics of Machine Learning
Types of machine learning: Supervised, unsupervised, and reinforcement learning
Common algorithms used in healthcare (e.g., decision trees, logistic regression)
Evaluating model performance using metrics like precision, recall, and F1-score
Addressing biases and limitations in machine learning models
Day 4: Data-Driven Strategies
Identifying actionable insights from healthcare datasets
Aligning data strategies with organizational objectives
Case study: Using predictive analytics to optimize hospital resource allocation
Workshop: Developing a data-driven strategic roadmap
Day 5: Artificial Neural Networks and Deep Learning
Understanding neural networks and their architecture
Applications of deep learning in medical imaging and diagnostics
Challenges in implementing deep learning models
Tools and platforms for building neural networks
