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Additional Registration Fee
Pre-Course

Deep Dive: AI in Critical Care Medicine 2026

Saturday, March 21, 2026
1:00 PM - 5:00 PM Central Time
Location: W185

Additional Registration Fee

AI is no longer just research; it is appearing in our daily practice. From sepsis prediction to imaging tools, ICU clinicians are beginning to see AI built into monitors, electronic health records, and decision support. While the promise is real, so are the risks. This course will help intensive care clinicians explore AI in a practical and relevant way, even without a technical background. Speakers will review how AI works in healthcare and discuss where bias can appear. Attendees will learn the language around AI and practical steps to apply when they encounter new tools in their units. Cases and exercises will show why an algorithm that works in one hospital may fail in another; how human-AI interactions can create new errors if we are not careful; what to watch for after an algorithm is deployed, since models can drift or change when they start influencing care; and real-world examples of algorithms that caused harm and lessons to take back to our hospitals.

Learning Objectives:

  • Explain what AI is and how it is used in critical care, including common myths and limitations
  • Recognize when an algorithm may not work the same way across hospitals or patient groups and describe how to monitor for these problems
  • Identify how AI advice can mislead clinicians and practice using forcing prompts or better explanations to reduce errors
  • Outline a basic monitoring plan for any algorithm in an ICU, including what to track, how often, and when to call for a review

Presentations:

  • 1:00 PM - 1:10 PM Central Time
    Welcome and Framing: Why Bias and Teaming Are the Crux This Year

    Anirban Bhattacharyya, MD, MS, MPH (he/him/his) – Mayo Clinic

  • 1:10 PM - 1:40 PM Central Time
    Introduction to AI in the ICU: Promise, Bias, and Pitfalls

    Chaoping Wu, MD – Cleveland Clinic Foundation

  • 1:40 PM - 2:25 PM Central Time
    When Fairness Doesn't Travel: Distribution Shift, Per-Group Performance, and Real-World Monitoring

    Judy W. Gichoya, MD, MS – Emory University

  • 2:25 PM - 3:10 PM Central Time
    Human-AI Teaming: Automation Bias, Forcing Functions, and Explanation Pitfalls

    Sharad Patel, MD

  • 3:10 PM - 3:25 PM Central Time
    Break
  • 3:25 PM - 4:10 PM Central Time
    When the Model Changes Care: Feedback Loops, Performativity, and Minimum Viable Monitoring

    Omar Badawi, PharmD, MPH, FCCM (he/him/his) – US Telemedicine and Advanced Technology Research Center

  • 4:10 PM - 4:40 PM Central Time
    AI Incident Rounds: What Went Wrong, What to Change on Monday?

    Anirban Bhattacharyya, MD, MS, MPH (he/him/his) – Mayo Clinic

  • 4:50 PM - 5:00 PM Central Time
    Wrap-Up

    Anirban Bhattacharyya, MD, MS, MPH (he/him/his) – Mayo Clinic