AI in EM - Harvard Medical Faculty Physicians Emergency Medicine CME Courses

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.

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
• Building clinical applications that leverage AI and language models

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.

AFTER THIS COURSE, LEARNERS WILL BE ABLE TO:

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.
Recognize best practices for evaluating and monitoring AI tools after deployment, including methods for measuring impact on diagnostic quality and equity.
Collaborate with operational leaders and IT partners to bring new AI-enabled tools into clinical systems responsibly.

++ No prior coding experience needed ++

European CME

An application has been submitted to the European Accreditation Council for Continuing Medical Education (EACCME®).

Through an agreement between the European Union of Medical Specialties (UEMS) and the American Medical Association, physicians may convert EACCME® credits to an equivalent number of AMA PRA Category 1 Credits™. Information on the process to convert EACCME® credit to AMA credit can be found here.

Live educational activities, occurring outside of Canada, recognized by the UEMS-EACCME® for ECMEC®s are deemed to be Accredited Group Learning Activities (Section 1) as defined by the Maintenance of Certification Program of the Royal College of Physicians and Surgeons of Canada.