ChatGPT, Mixtral AI systems show promise in detecting healthcare-associated infections

Editor's Note

Based on the performance of two specific systems in detecting healthcare-associated infections (HAIs) in a recent study, artificial intelligence (AI) could help providers enhance surveillance, streamline tasks, and free staff to focus on patient care.

Published March 14 in The American Journal of Infection Control, the study assessed 2 AI agents, OpenAI's chatGPT plus (GPT-4) and a Mixtral 8×7b-based local model, for their ability to identify Central Line-Associated Bloodstream Infection (CLABSI) and Catheter-Associated Urinary Tract Infection (CAUTI) from 6 National Health Care Safety Network training scenarios.

“Both AI models accurately identified CLABSI and CAUTI in all scenarios when given clear prompts,” researchers wrote. “Challenges appeared with ambiguous prompts including Arabic numeral dates, abbreviations, and special characters, causing occasional inaccuracies in repeated tests.”

However, the authors emphasize that effective HAI surveillance requires a refined AI model as well as a focus on educating users who will oversee the system.