This module introduces the fundamental concepts of Artificial Intelligence, Machine Learning, and Deep Learning. Learners also explore the healthcare data ecosystem, including electronic health records (EHRs), medical imaging, genomics, and wearable data. Real-world business case examples illustrate how AI is applied across various data types in the healthcare domain.

Covers essential machine learning methods such as regression, classification, clustering, and model evaluation. The focus is on how these techniques are used to extract insights from health data. The module includes simple examples and case studies to build a solid understanding of ML in clinical contexts.

Examines how AI is currently integrated into clinical workflows. Topics include AI in diagnosis, chronic disease management, hospital resource optimization, and decision support systems. Through real clinical examples and case studies, learners explore the challenges and opportunities of applying AI in everyday patient care.

Introduces Generative AI tools such as ChatGPT and DALL·E, along with Agentic AI systems that can plan and act autonomously in healthcare contexts. The module also covers model interpretability frameworks like SHAP and LIME. Learners explore responsible AI use in healthcare, focusing on bias mitigation, fairness, and ethical deployment. Emphasis is placed on building transparent, trustworthy, and clinically reliable AI systems.

Focuses on the application of AI in remote monitoring, mobile health platforms, and early screening tools. Learners explore how AI enhances wearable devices and mobile diagnostics, making healthcare more preventive, personalized, and accessible. Real-world product evaluations are included.

Covers the legal, ethical, and operational considerations of deploying AI systems in healthcare. Topics include patient data privacy, fraud detection, and compliance with industry standards such as HL7 and FHIR. Learners gain insights into how AI systems are introduced and scaled within healthcare organizations.

Guides learners through the process of conceptualizing and presenting AI-based healthcare innovations. Topics include business model development, solution validation, stakeholder communication, and investor pitching. This module prepares learners to transition ideas into viable AI products or services.

Participants synthesize their learning by developing and pitching a complete AI solution targeting a real healthcare challenge—such as diagnosis, monitoring, or hospital efficiency. The capstone includes mentorship and feedback from faculty and peers to ensure practical and strategic alignment.