Module 1: Foundations of Artificial Intelligence in Healthcare

• Introduction to AI, Machine Learning (ML), and Deep Learning (DL)

• Evolution of AI applications in medicine and health informatics

• Understanding datasets and algorithms for healthcare AI

• Practical: Hands-on demo Predictive analytics using sample health datasets

Module 2: Emerging Technologies Driving Digital Health Innovation

• Internet of Medical Things (IoMT) and sensor-based data collection

• Blockchain for health data integrity and interoperability

• Robotics and automation in clinical care

• Virtual, Augmented, and Mixed Reality (VR/AR/MR) in medical training

• Practical: Case review of IoMT integration in remote patient monitoring

Module 3: Designing and Implementing AI Systems in Healthcare

• Building data pipelines for AI models

• Integration with EHRs and HIS systems

• Evaluating model performance and clinical relevance

• Challenges in adoption and interoperability

• Practical: Developing a conceptual workflow for an AI-based triage system

Module 4: Ethics, Privacy, and Governance of AI in Healthcare

• Ethical dilemmas in AI-driven decision-making

• Bias, fairness, and accountability in machine learning models

• Privacy-preserving AI and federated learning

• Regulatory frameworks and compliance (GDPR, HIPAA, WHO Ethics Guidelines)

• Practical: Reviewing ethical case studies in AI healthcare applications

Module 5: Future of Healthcare Informatics and Innovation Roadmap

• Trends shaping the future of AI in healthcare

• Integrating AI with big data, genomics, and precision medicine

• Developing institutional AI transformation roadmaps

• Scaling innovations for LMIC healthcare systems

• Practical: Group project Drafting an AI adoption strategy for a healthcare organization