In this module, you will learn about the basics of artificial intelligence in healthcare. The module begins with tracing the historical evolution of AI, followed by machine learning concepts and how these technologies are transforming clinical workflows across diagnosis, treatment, and patient care. Through real-world examples, you will learn how AI is being integrated into healthcare. You will gain insights into the opportunities and limitations presented by this integration. The module concludes with a forward-looking discussion on the challenges, innovations, and future trends in AI-driven healthcare, preparing you to think critically about the role of AI in modern medical practice.

This module addresses the ethical, legal, and regulatory dimensions of AI implementation in healthcare settings. Students will examine fundamental ethical principles, including autonomy, beneficence, and justice, as they apply to AI-assisted medical decision-making and patient care. The module provides comprehensive coverage of bias detection and mitigation strategies, helping students understand how algorithmic fairness impacts health equity and patient outcomes across diverse populations. Students will explore privacy-preserving AI technologies and cybersecurity frameworks essential for protecting sensitive health information in AI systems. The module also covers the global regulatory landscape, including FDA guidance and international standards, while providing practical frameworks for establishing AI governance and risk management processes within healthcare organizations.

This hands-on module provides students with practical skills for developing and implementing machine learning solutions in healthcare environments. Students will master the complete ML workflow from problem definition to model development, with special emphasis on healthcare-specific considerations such as regulatory compliance and clinical validation requirements. The module covers both supervised and unsupervised learning techniques through real-world medical applications, including diagnostic prediction, patient segmentation, and clinical outcome forecasting. Students will learn advanced feature engineering techniques for medical data. The module concludes with practical guidance on integrating ML models into clinical decision support systems, addressing implementation barriers, and measuring clinical impact in real healthcare settings.

This final module consolidates the knowledge gained throughout the course and guides learners through a comprehensive, hands-on application of AI in a healthcare scenario. Learners will revisit key concepts, engage in a case-based project or lab, and demonstrate their understanding through practical problem-solving. The module also includes a final assessment and offers reflection activities to help learners identify future learning pathways and career opportunities in healthcare AI. Emphasis is placed on real-world relevance, ethical practice, and readiness for continued specialization. This capstone experience reinforces both conceptual mastery and practical competence.