ACG 2025: Large Language Models for Eosinophilic Esophagitis Detection

We are presenting a poster at the American College of Gastroenterology (ACG) Annual Scientific Meeting, which will be held October 24–29, 2025, in Phoenix, AZ.

Poster Presentation

Title: Large Language Models Combined with a Single Diagnostic Code Detect Eosinophilic Esophagitis in Electronic Health Records with High Accuracy

Authors:

• Corey James Ketchem
• Ugurcan Vurgun
• Andrew Wang
• Sarah Thomas
• Alexis Batugo
• Gary W. Falk
• Kristel L. Lynch
• Evan S. Dellon
• Danica L. Mowery
• James D. Lewis

Publication: American Journal of Gastroenterology. 2025; Abstract P2739.

Research Focus: This study demonstrates the effectiveness of large language models (LLMs) combined with minimal diagnostic coding for identifying eosinophilic esophagitis (EoE) cases in electronic health records.

Key Innovation: The method leverages natural language processing to extract meaningful clinical information from EHR text, reducing the need for extensive manual chart review while maintaining high detection accuracy.

Impact: This work has implications for:

• Automated case identification in retrospective studies
• Population health surveillance for rare diseases
• Reducing manual chart review burden in clinical research
• Scalable EHR-based phenotyping strategies

If you’re interested in this research or potential collaborations in AI applications for gastroenterology, please don’t hesitate to reach out.