Build, Deploy, and Govern AI Applications Across Healthcare Settings

This pre-work module introduces learners to the fundamentals of AI and machine learning, and explores how key technologies are applied across clinical, administrative, and operational settings within the healthcare landscape. It also covers the differences between general-purpose LLMs and specialized healthcare models, building the foundational understanding required for real-world application. In addition, the module orients learners to the AI and automation tools used throughout the program, guiding account setup, initial prompt execution, and the selection of appropriate tools for specific healthcare tasks.

In this module, learners will understand the Clinical AI lifecycle and the role of generative and agentic AI in healthcare settings. They will explore how AI can be safely applied across clinical workflows, identify high-impact use cases, and develop skills in prompt engineering for medical tasks. Additionally, learners will be introduced to AI-assisted coding and the foundational concepts behind LLMs, AI agents, and RAG systems, enabling them to evaluate, design, and begin building intelligent healthcare applications.

In this module, learners will apply AI and machine learning techniques to real clinical and healthcare operations use cases. They will work with predictive models, clinical decision support systems, and healthcare data workflows to understand how AI is deployed in practice. The module also focuses on evaluation, validation, and responsible deployment of AI systems, including fairness, interpretability, and regulatory considerations in real-world healthcare environments.

In this module, learners will understand how AI systems are operationalized within real healthcare environments, moving from prototype to production. They will explore how AI tools are integrated with EHR systems using interoperability standards, scaled across hospital networks, and governed under enterprise-grade deployment frameworks. The module emphasizes real-world deployment through Epic integration, workflow orchestration, and healthcare AI governance.

Build a real-world healthcare AI use case after completing all 9 weeks of coursework. Learners apply their learning to a Capstone Project that takes a real healthcare AI use case from problem framing to a governance-ready deployment plan.

This masterclass helps learners position themselves as AI adoption accelerates across clinical, operational, and administrative settings. Drawing on the program's emphasis on human intuition and strategic judgment, faculty will map the emerging roles at the intersection of clinical practice, data, and AI implementation, from clinical informatics and AI validation to deployment and governance-focused roles. Learners will explore how to translate their existing clinical, technical, or leadership background into this space, which skills and credentials carry weight, and how a university credential and a portfolio of real-world projects strengthen their profile. The session closes with practical guidance on charting a credible path forward in a field where demand is growing faster than proven expertise.

This masterclass looks ahead at the technologies reshaping how care is delivered and managed. Faculty will examine developments such as generative AI for clinical documentation and decision support, multimodal models that reason across imaging, text, and other data sources, and agentic systems that work alongside care teams to automate complex workflows. Beyond the technology itself, learners will explore what it takes to move innovations from promising pilots into real clinical use, including the evidence, validation, regulatory, and workflow considerations that determine whether an innovation holds up in practice. Learners will leave with a grounded view of what is genuinely new, where the field is heading, and how to evaluate emerging tools with appropriate rigor.

Designed for technical professionals across the stack, this self-paced course delivers comprehensive training in Claude - Anthropic's industry-leading AI platform. Participants gain fluency across the full development lifecycle: API integration, model selection, agentic workflow design with Claude Code, tool integration via the Model Context Protocol, and enterprise deployment with cost management and Anthropic's Constitutional AI framework. Note : Learners are required to have a Claude Subscription to complete this Self-Paced Module

This masterclass covers the Anthropic AI landscape, exploring Claude models, Constitutional AI, and key safety and alignment principles. Learners will apply effective Prompt Engineering techniques, use the Claude API for tasks and integrations, generate structured outputs, build simple applications, compare Claude with other AI models, and evaluate ethical considerations for deploying AI systems.