ArticleAntimicrobial stewardship & healthcare epidemiology : ASHE2025
Evaluating chain-of-thought prompting in a GPT chatbot for BCID2 interpretation and stewardship: how does AI compare to human experts?
Article in Antimicrobial stewardship & healthcare epidemiology : ASHE, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Error Detection and Correction in Chinese Radiology Reports Using Large Language Models: Real-World Clinical Validation Study.Journal of medical Internet research · 2026Article
- Artificial Intelligence in Infectious Disease Care: Selected Applications in Tuberculosis, Sepsis, and Antimicrobial Stewardship.Diagnostics (Basel, Switzerland) · 2026Review
- Large Language Models in Infectious Diseases: A Systemic Review.Research square · 2026Article
- Evolution of technology and disparities in antimicrobial stewardship: a narrative review.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rapid molecular diagnostics, such as the BIOFIRE® Blood Culture Identification 2 (BCID2) panel, have improved the time to pathogen identification in bloodstream infections. However, accurate interpretation and antimicrobial optimization require Infectious Disease (ID) expertise, which may not always be readily available. GPT-powered chatbots could support antimicrobial stewardship programs (ASPs) by assisting non-specialist providers in BCID2 result interpretation and treatment recommendations. This study evaluates the performance of a GPT-4 chatbot compared to ASP prospective audit and feedback interventions. Methods: This prospective observational study assessed 43 consecutive real-world cases of bacteremia at a 399-bed VA Medical Center from January to May 2024. The GPT-chatbot utilized "chain-of-thought" prompting and external knowledge integration to generate recommendations. Two independent ID physicians evaluated chatbot and ASP recommendations across four domains: BCID2 interpretation, source control, antibiotic therapy, and additional diagnostic workup. The primary endpoint was the combined rate of harmful or inadequate recommendations. Secondary endpoints assessed the rate of harmful or inadequate responses for each domain. Results: The chatbot had a significantly higher rate of harmful or inadequate recommendations (13%) compared to ASP (4%, Conclusions: GPT-powered chatbots show potential for supporting antimicrobial stewardship but should only complement, not replace, human expertise in infectious disease management.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.