Build, Deploy, and Govern AI Applications Across Healthcare Settings

The live masterclasses are led by world-renowned Johns Hopkins University faculty with expertise in healthcare AI, clinical informatics, public health, engineering, and AI governance. You'll learn from Daniel Byrne, Faculty Member at the Whiting School of Engineering and Bloomberg School of Public Health; Dr. Ian McCulloh, Manager of Artificial Intelligence Continuing and Executive Education; Dr. Robert Stevens, Director of the Division of Informatics, Integration and Innovation and Precision Medicine; and Dr. Ahmed Hassoon, Assistant Research Professor in the Bloomberg School of Public Health.
In addition, industry practitioners such as Sharath M S, Staff AI Engineer at GE HealthCare; Rishov Chatterjee, Director of Data Science and AI at Integra Connect; and G. Anthony Reina, Head of Machine Learning at a stealth biotech startup, provide mentorship and practical insights into real-world healthcare AI applications.

Learners gain hands-on experience with widely used AI tools including:
● ChatGPT
● Claude
● Gemini
● Python
● Google Colab
● Jupyter
● Codex
● n8n
● Epic Sandbox
● FHIR / CDS Hooks
● Claude Code
The program demonstrates how these tools support clinical documentation, AI-assisted
coding, workflow automation, and healthcare analytics.

Yes. The curriculum includes Clinical Decision Support Systems (CDSS), predictive modeling, patient risk assessment, survival analysis, healthcare data preparation, and AI evaluation frameworks that support safer clinical decision-making.

Yes. The program begins by explaining how AI chatbots support conversational tasks such as clinical documentation, information retrieval, and administrative assistance. It then progresses to Agentic AI, where AI systems can reason, use external tools, access healthcare data, and orchestrate multi-step workflows.
You'll learn how Agentic AI goes beyond simple conversations to automate real-world healthcare processes such as clinical decision support, prior authorization, revenue cycle management, and EHR-integrated workflows using technologies like RAG, n8n, and AI agents, all while maintaining appropriate human oversight and governance.

This program is designed for:
● Medical practitioners and clinicians
● Healthcare researchers
● Hospital administrators and healthcare leaders
● Healthcare consultants
● Healthcare technology professionals
● Compliance officers and policymakers
It is ideal for professionals looking to implement AI responsibly across healthcare organizations, and no software engineering or prior coding experience is required.

Non-technical health system managers can build practical AI tools using AI-assisted coding, Vibe Coding, and no-code platforms instead of writing software from scratch. Through Vibe Coding—using natural language prompts to generate, explain, and debug Python code with AI—they can leverage tools like ChatGPT, Claude, Google Colab, Jupyter Notebooks, n8n, and Codex to prototype clinical AI workflows, automate healthcare processes, and develop practical AI solutions. The program provides guided, hands-on exercises that help learners build and implement AI workflows while focusing on clinical strategy and operational decision-making rather than programming.

Please contact a Program Advisor regarding fee structure, payment plans, available offers, and financial assistance options.

Most learning activities use free versions of the required tools. API credits and paid tool access required for specific modules are provided at no additional cost. Learners require Claude subscription for the self-paced module.

AI agents assist with medical coding by automating complex administrative tasks within revenue cycle workflows. Specifically, multi-agent systems and Retrieval-Augmented Generation (RAG) frameworks are used to automate the assignment of ICD-10 and CPT codes, which streamlines broader processes like claims processing and payer policy interpretation. While these agentic frameworks significantly reduce manual effort, it is emphasized that human oversight remains critical to ensure accuracy and compliance.

The program combines foundational AI concepts with hands-on projects to help you implement multimodal AI workflows and Agentic AI in healthcare settings. You'll learn how multimodal AI supports clinical decision-making across text and medical imaging, build AI agents using tools like n8n and Claude and automate workflows such as medical coding and revenue cycle management. The curriculum also covers integrating AI with EHR systems like Epic using FHIR and CDS Hooks, scaling AI safely across healthcare organizations, and applying governance frameworks such as HIPAA, the EU AI Act, and FDA guidance. You'll conclude with a capstone project that develops an implementation-ready AI solution for a real-world clinical use case.

You can use n8n as a low-code/no-code tool to build automated workflows that connect AI systems to external healthcare data sources and Electronic Health Record (EHR) systems. Through hands-on projects, you'll learn how to create agentic AI workflows that connect AI models to external healthcare data sources and explore integration with Epic testing environments using interoperability standards such as FHIR and CDS Hooks. These practical exercises show you how to automate clinical and administrative workflows in real-world healthcare environments.

Practicing clinicians can integrate AI into existing clinical workflows by embedding AI-powered decision support within EHR systems like Epic, reducing administrative tasks such as documentation and prior authorization, and using explainable AI to support faster, more informed clinical decisions. The program also teaches clinicians how to build practical AI tools with ChatGPT, Claude, and AI-assisted coding, while applying human-in-the-loop practices to ensure AI augments clinical judgment rather than replacing it.