Evidence map›Paper›PMID 40955356›Full record

ArticleArtificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )2025

Comparative Evaluation of Clinical Large Language Models and Machine Learning to Predict Antimicrobial Resistance in Hospital-Onset Sepsis.

Scott A Cohen, Ziyi Chen, Jiang Bian, Christina Boucher, Yonghui Wu, Mattia Prosperi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Scott A CohenDepartment of Epidemiology, University of Florida, Gainesville, FL, USA.ORCID 0000-0002-8758-180X
Ziyi ChenDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Jiang BianBiostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
Christina BoucherDepartment of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA.
Yonghui WuDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Mattia ProsperiDepartment of Epidemiology, University of Florida, Gainesville, FL, USA.

Funding

Artificial Intelligence and Counterfactually Actionable Responses to End HIV (AI-CARE-HIV)R01AI172875 · NIAID · UNIVERSITY OF FLORIDA · PI Jiang Bian, Mattia Prosperi · 2023 to 2026
$2.8M
Developing Computational Methods for Surveillance of Antimicrobial Resistant AgentsR01AI141810 · NIAID · UNIVERSITY OF FLORIDA · PI BOUCHER, CHRISTINA, PROSPERI, MATTIA · 2019 to 2023
$2.1M
Utilizing phylodynamic and causal artificial intelligence methods for predicting and characterizing antibiotic resistant bacterial transmission within and beyond hospital settingsF30AI183562 · NIAID · UNIVERSITY OF FLORIDA · PI Scott A Cohen · 2025 to 2026
$102k
NIAID NIH HHS F30 AI183562NIAID NIH HHS R01 AI141810NIAID NIH HHS R01 AI172875
6 · The paper itself

Abstract

Approaches to guide empiric antimicrobial therapy are needed, especially in critically ill populations with prevalent antimicrobial resistance (AMR). While artificial intelligence shows promise in predicting AMR, scalable and generalizable prediction models are essential for broad clinical adoption. We utilized a publicly available clinical large language model (LLM), Gatortron, in comparison to traditional machine learning, to predict AMR and methicillin-resistant

Indexed as

antimicrobial resistancelarge language modelmachine learningpredictive modellingsepsis

Identifiers

PMID40955356
PMCPMC12433606

What OpenQuestion holds

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Registered trials

None linked

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.