Citations 3
Patient Testing of a Remote Stethoscope in Simulation Lab: A Case Study for Usability and AI Integration Readiness
DeBaise, Kimberly, Abdulbari, Jihan A., Arnold, Jacqueline J., Brereton, Tracey, Foy, Denise, Huber, Joe, Juhn, Young, Lifson, Mark, Malik, Momin, Melnick, Joe , Ohde, Joshua, Salib, Ingy, Spiten, Matthew J., Watson, Dave , Wi, Chung-Il, Overgaard, Shauna, Brereton, Tracey A. , Foy, Denise, Huber, Joe, Juhn, Young J. , Ohde, Joshua W. , Spiten, Matthew J., Overgaard, Shauna M.
Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care | August 19, 2026 | Volume: 15 | Issue: 1 | Pages: 38-39
Building a Simulation-Based Evaluation Framework for AI-Enabled Medical Devices
DeBaise, Kimberly, Melnick, Joe , Foy, Denise, Huber, Joe, Salib, Ingy, Wi, Chung-Il, Lifson, Mark, Overgaard, Shauna, Foy, Denise, Huber, Joe, Overgaard, Shauna M.
Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care | August 19, 2026 | Volume: 15 | Issue: 1 | Pages: 176-177
Colon-Pilot: A Generative AI Tool for Automated Colonoscopy Surveillance Recommendations and 2024 ACG/ASGE Quality Benchmarking
Garg, Sushil Kumar , Mau, Brayden , Hubers, Jeffery , Arce, Victor, Hooper, Nicole, Lindquist, Jennifer, Harper, Sarah B. , Thayer, Ann B., Mukherjee, Piyush, Loufek, Brenna, Melnick, Joe , Rost, Lauren, Robbins, Steven A. , Chaudhary, Rajeev, Harper, Sarah B , Loufek, Brenna T , Rost, Lauren M , Gomes, Rahul , Chaudhary, Rajeev , Hubers, Jeffrey , Arce, Victor, Mukherjee, Piyush, Rost, Lauren M. , Gomes, Rahul
American Journal of Gastroenterology | February 10, 2026 | Volume: 121 | Issue: 4 | Pages: 964-973
INTRODUCTION: High-quality colonoscopy requires accurate risk stratification for surveillance per the 2020 US Multi-Society Task Force guidelines and adherence to 2024 American College of Gastroenterology/American Society for Gastrointestinal Endoscopy quality benchmarks. Both are operationally challenging in clinical practice. We developed and validated Colon-Pilot, a large language model—powered clinical decision support system using GPT-4o to automate and standardize both functions. METHODS: The system was evaluated in 2 operational modes: (i) a human-in-the-loop clinical decision support validation of surveillance recommendations for 596 colonoscopies, comparing concordance with 2020 US Multi-Society Task Force guidelines against expert consensus, and (ii) an automated administrative audit applying Colon-Pilot to 42,632 colonoscopies across the Mayo Clinic Health System to calculate 2024 American College of Gastroenterology/American Society for Gastrointestinal Endoscopy priority quality indicators. Recommendations were autogenerated unless predefined safety criteria triggered manual review. RESULTS: Colon-Pilot issued recommendations for 522 of 596 cases (87.6%) and flagged 12.4% for manual review. For automated cases, guideline-concordant accuracy was 97.5% (Cohen κ = 0.970) versus 69.7% (κ = 0.781) for original endoscopist recommendations. Discordant artificial intelligence (AI) cases (n = 13) most often recommended longer-than-appropriate intervals (62%). Applied to the enterprise data set, Colon-Pilot calculated performance exceeding 2024 targets: adenoma detection rate 49.8% (≥35%), sessile serrated lesion detection rate 17.7% (≥6%), bowel preparation adequacy 91.8% (≥90%), and cecal intubation rate 97.4% (≥95%). DISCUSSION: Colon-Pilot demonstrated high fidelity in applying surveillance guidelines and automated quality benchmarking, outperforming unassisted endoscopists in guideline adherence. By combining safety protocols with large-scale automated reporting, it offers a scalable solution for improving both efficiency and quality in colorectal cancer prevention.