AI 500 - Artificial Intelligence in Health Care: Foundations & Knowledge Applications

Course overview

This CE course provides an in-depth exploration of Artificial Intelligence (AI) as it applies to the modern healthcare ecosystem, grounding learners in both the historical foundations and contemporary realities of AI-enabled healthcare. Drawing directly from the introductory concepts of the course, learners are introduced to AI not as a replacement for human judgment, but as a powerful tool designed to augment clinical, operational, and administrative decision-making. The course emphasizes the evolution of algorithms, data-driven reasoning, and computational thinking as the backbone of modern AI systems, setting the stage for responsible and effective application in healthcare environments. The introduction frames AI within healthcare as a socio-technical system, one that exists at the intersection of technology, people, processes, and ethics. Learners explore why healthcare presents unique challenges for AI adoption, including data complexity, variability in clinical workflows, patient safety considerations, and the high-stakes nature of medical decision-making. Rather than focusing solely on technical development, the course highlights the importance of understanding context, governance, and human oversight as essential components of successful AI integration. A central theme of the course is augmented intelligence, underscoring the role of AI as a collaborator that supports clinicians, nurses, administrators, and health system leaders. The introduction establishes how AI can enhance pattern recognition, predictive capability, and operational efficiency while preserving the critical role of human expertise, empathy, and accountability. Learners are encouraged to critically assess both the promise and limitations of AI technologies, developing a balanced perspective grounded in evidence rather than hype. The course description further reflects the introduction's emphasis on ethical responsibility, transparency, and trust. Learners are introduced early to key concerns such as data privacy, algorithmic bias, explainability, and patient autonomy. These concepts are positioned not as afterthoughts, but as foundational principles that must guide AI development and deployment across all healthcare settings. By grounding ethical considerations in real-world healthcare contexts, the course prepares learners to anticipate and mitigate unintended consequences of AI adoption. Ultimately, this course equips learners with a conceptual framework to understand AI's role in healthcare today and its trajectory for the future. Building on the introduction, the course prepares students and professionals to engage thoughtfully with AI technologies, evaluate their appropriateness for specific healthcare use cases, and participate in informed decision-making as clinicians, administrators, educators, or leaders. The course sets the foundation for deeper exploration of AI applications, governance, and innovation while reinforcing the central principle that effective healthcare AI must remain human-centered, evidence-based, and mission-driven.