AI in Emergency Medicine
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Pace
Instructor-led
Subject
Difficulty
- Advanced
Credit
CE/CME Certificate
Platform
Continuing Education from Harvard Medical School
What you'll learn
Describe the core architecture and evolution of modern AI models, including large language models and agentic systems.
Identify clinical use cases in emergency medicine where AI can improve diagnostic accuracy, triage quality, documentation, and operational performance.
Apply evidence-based prompting strategies to interact effectively with clinical AI tools and evaluate the reliability of their outputs.
Assess risks, limitations, and failure modes of AI tools in acute and time-sensitive clinical environments.
Discuss governance, regulatory, ethical, and medicolegal considerations involved in deploying AI for patient care.
Implement strategies for safe integration of AI into local workflows, including oversight, documentation standards, and quality review.
Course description
AI in Emergency Medicine is a two-day, 15-hour continuing medical education course designed to equip frontline clinicians, educators, and health-system leaders with a foundational understanding and practical skills for applying artificial intelligence safely and effectively in the emergency care environment.
Day One focuses on core concepts, architecture, and governance of clinical AI—preparing participants to understand how modern models work, how to evaluate them, and how to manage the organizational, legal, and ethical implications of deploying AI-enabled tools.
Day Two is a hands-on practicum, emphasizing real-world use cases, clinical prompting, workflow integration, triage and safety applications, documentation and revenue cycle tools, and operational considerations for bringing AI solutions into emergency departments and acute-care settings.
The goal of this course is to prepare clinicians to critically evaluate, safely integrate, and thoughtfully supervise AI technologies in emergency care, including:
- Understanding the architecture and capabilities of contemporary AI models
- Identifying opportunities and risks in clinical workflows
- Applying best practices for prompting, oversight, documentation, and patient communication
- Recognizing governance, regulatory, and ethical frameworks needed for safe implementation
- Evaluating clinical quality implications and using AI for diagnostic accuracy, triage quality, and operational performance
This course is relevant for clinicians working in emergency medicine, critical care, hospital medicine, urgent care, internal medicine, and family medicine, as well as operational leaders, quality officers, and educators who supervise or evaluate AI-enabled clinical tools.
