Department Announces Emergency Medicine AI Workshop in Paris, France - April 2027
The BIDMC Department of Emergency Medicine has added an AI in Emergency Medicine Workshop to its 2027 international live CME offerings. LEARN MORE AND REGISTER
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 ++