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Overview

Artificial intelligence is rapidly transforming clinical practice, from ambient documentation to clinical decision support (CDS), yet most clinicians lack a practical framework for evaluating and using these tools safely. This session introduces key AI concepts, including large language models (LLMs) and natural language processing, through a clinical lens tailored for clinicians. We'll demonstrate where these tools help and where they fall short, including how to verify AI output and protect patient information. Attendees will leave with a working understanding of where AI evidence is strongest, where key limitations lie, and how to critically evaluate AI tools in their own practice. A Q&A follows.

Learning Objectives

  • Define key AI terminology — including LLMs, NLP, generative AI, and ambient scribes — and explain how each applies to clinical medicine
  • Describe current, practical applications of AI tools in clinical medicine including ambient documentation and clinical decision support
  • Identify potential risks of AI tools in clinical practice, including hallucination, bias, and over-reliance
  • Apply privacy and professional-responsibility principles when using AI tools, including appropriate handling of protected health information (PHI)
  • Summarize the current trajectory of clinical AI in 2026, including regulatory developments, the rise of agentic AI workflows, and the growing focus on real-world performance over benchmark scores