Can we rely on artificial intelligence to guide antimicrobial therapy? A systematic literature review

Sulwan AlGain; Alexandre Marra; Takaaki Kobayashi; Pedro Marra; Patricia Deffune Celeghini; Mariana Kim Hsieh; Mohammed Abdu Shatari; Samiyah Althagafi; Maria Alayed; Jamila Ranavaya; Nicole Boodhoo; Nicholas Meade; Daniel Fu; Mindy Sampson; Guillermo Rodriguez-Nava; Alex Zimmet; David Ha; Mohammed Alsuhaibani; Boglarka Huddleston; Jorge Salinas

doi:10.1017/ash.2025.47

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Can we rely on artificial intelligence to guide antimicrobial therapy? A systematic literature review

Journal article

Open access

Peer reviewed

Can we rely on artificial intelligence to guide antimicrobial therapy? A systematic literature review

Sulwan AlGain, Alexandre Marra, Takaaki Kobayashi, Pedro Marra, Patricia Deffune Celeghini, Mariana Kim Hsieh, Mohammed Abdu Shatari, Samiyah Althagafi, Maria Alayed, Jamila Ranavaya, …

Antimicrobial stewardship & healthcare epidemiology : ASHE, Vol.5(1), e90

01/01/2025

DOI: 10.1017/ash.2025.47

PMCID: PMC11986881

PMID: 40226293

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Abstract

Background:Artificial intelligence (AI) has the potential to enhance clinical decision-making, including in infectious diseases. By improving antimicrobial resistance prediction and optimizing antibiotic prescriptions, these technologies may support treatment strategies and address critical gaps in healthcare. This study evaluates the effectiveness of AI in guiding appropriate antibiotic prescriptions for infectious diseases through a systematic literature review.Methods:We conducted a systematic review of studies evaluating AI (machine learning or large language models) used for guidance on prescribing appropriate antibiotics in infectious disease cases. Searches were performed in PubMed, CINAHL, Embase, Scopus, Web of Science, and Google Scholar for articles published up to October 25, 2024. Inclusion criteria focused on studies assessing the performance of AI in clinical practice, with outcomes related to antimicrobial management and decision-making.Results:Seventeen studies used machine learning as part of clinical decision support systems (CDSS). They improved prediction of antimicrobial resistance and optimized antimicrobial use. Six studies focused on large language models to guide antimicrobial therapy; they had higher prescribing error rates, patient safety risks, and needed precise prompts to ensure accurate responses.Conclusions:AI, particularly machine learning integrated into CDSS, holds promise in enhancing clinical decision-making and improving antimicrobial management. However, large language models currently lack the reliability required for complex clinical applications. The indispensable role of infectious disease specialists remains critical for ensuring accurate, personalized, and safe treatment strategies. Rigorous validation and regular updates are essential before the successful integration of AI into clinical practice.

Algorithms

Antibiotics

Antimicrobial Agents

Cohort Analysis

Drug Resistance

Infectious Diseases

Machine Learning

Accuracy

Artificial intelligence

Chatbots

Clinical decision making

Cross-sectional studies

Decision making

Decision support systems

Disease management

Large language models

Literature reviews

Medical practices

Performance assessment

Pneumonia

Quasi-experimental methods

Systematic review

Details

Title: Subtitle: Can we rely on artificial intelligence to guide antimicrobial therapy? A systematic literature review
Creators: Sulwan AlGain - Stanford University
Alexandre Marra - University of Iowa
Takaaki Kobayashi - University of Kentucky
Pedro Marra - University of California, San Francisco
Patricia Deffune Celeghini - Hospital Israelita Albert Einstein
Mariana Kim Hsieh - University of Iowa
Mohammed Abdu Shatari - King Saud Medical City
Samiyah Althagafi
Maria Alayed
Jamila Ranavaya - University of Kentucky
Nicole Boodhoo - University of Iowa
Nicholas Meade - University of Kentucky
Daniel Fu - University of Chicago
Mindy Sampson - Stanford University
Guillermo Rodriguez-Nava - Stanford University
Alex Zimmet - Stanford University
David Ha - Stanford University
Mohammed Alsuhaibani - King Faisal Specialist Hospital & Research Centre
Boglarka Huddleston - Stanford University
Jorge Salinas - Stanford University
Resource Type: Journal article
Publication Details: Antimicrobial stewardship & healthcare epidemiology : ASHE, Vol.5(1), e90
DOI: 10.1017/ash.2025.47
PMID: 40226293
PMCID: PMC11986881
NLM abbreviation: Antimicrob Steward Healthc Epidemiol
ISSN: 2732-494X
eISSN: 2732-494X
Publisher: Cambridge University Press; CAMBRIDGE
Language: English
Date published: 01/01/2025
Academic Unit: Infectious Diseases; Internal Medicine
Record Identifier: 9984804801102771

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