Course Overview
This course covers a broad overview and introductory level of history, foundational concepts, and methods of artificial intelligence (AI), focusing on public health and healthcare applications. It includes hands-on practice.
Outcomes
This course contextualizes historical and methodological topics of AI in public health, healthcare, and their research applications. It enriches our educational program covering ‘next-generation data science’ in compliance with up-to-date accreditation standards and with translational relevance to public health, healthcare, and professional practice.
Course Objectives
Upon successful completion of the course, participants will be able to:
- Explain the evolution of AI in public health and healthcare: Discuss the history and impact of AI, particularly in the context of public health statistics and methodologies.
- Identify and apply AI methodologies: Understand fundamental AI techniques, including supervised learning, unsupervised learning, and neural networks, and evaluate their applications in public health and healthcare scenarios.
- Evaluate AI model performance: Critically assess the appropriateness and accuracy of AI models in health-related settings through validation techniques.
- Address ethical and bias considerations: Discuss the health implications of AI, including issues of bias, fairness, and causality within AI modeling.
- Analyze real-world applications: Provide examples of AI use cases in public health and healthcare, such as disease surveillance, predictive analytics, personalized medicine, and healthcare logistics optimization