The Smartest Intern You'll Ever Meet: Why Framing Matters as much as the Question When Dealing with AI Systems

The progression from medical school to residency takes medical knowledge of physiology and pathophysiology and puts it into practice. Most of us consider ourselves to be lifelong learners. AI/LLMs (Large Language Models) have been fed an incredible amount of knowledge but will they ever graduate residency? This talks focuses on why you should never fully abdicate your thinking to AI and how the FRAMING is just as important as WHAT you want to know when interacting with the smartest intern (AI) you will ever have.

The session, sponsored by the SAEM Academy for Diversity and Inclusion in Emergency Medicine (ADIEM) Education Committee, will be led by Kivan Polimis, a data scientist and machine learning engineer with extensive experience designing and deploying advanced machine learning and data science solutions across healthcare, finance, and public policy. He has led the development of production ML pipelines, NLP-driven classification systems, and cloud-native data infrastructures that scale for high-throughput, real-time analytics. Kivan has applied AI and predictive modeling to optimize operations, reduce costs, and deliver actionable insights, including multi-million-dollar efficiency gains in shipping and patient care workflows. In addition to his industry work, he teaches data science and AI ethics at the University of Washington, sharing his expertise in responsible, impactful analytics.