Evidence map›Paper›PMID 41424120›Full record

ReviewThe Journal of antimicrobial chemotherapy2026

Artificial intelligence for improving decision-making in bacterial infection management: a narrative review.

Anisia Talianu, Oskar Fraser-Krauss, William Bolton, Damien Ming, Nina Zhu, Bernard Hernandez, Mark Gilchrist, Alison Holmes, Pantelis Georgiou, Timothy Miles Rawson

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The Journal of antimicrobial chemotherapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Anisia TalianuFaculty of Engineering, Department of Computing, Imperial College London, London, UK.
Oskar Fraser-KraussFaculty of Engineering, Department of Computing, Imperial College London, London, UK.
William BoltonFaculty of Engineering, Department of Computing, Imperial College London, London, UK.
Damien MingCentres for Antimicrobial Optimisation Network, Imperial College London, London, UK.
Nina ZhuCentres for Antimicrobial Optimisation Network, Imperial College London, London, UK.
Bernard HernandezCentres for Antimicrobial Optimisation Network, Imperial College London, London, UK.
Mark GilchristThe Fleming Initiative, Imperial College London, London, UK.ORCID 0009-0003-0625-3535
Alison HolmesCentres for Antimicrobial Optimisation Network, Imperial College London, London, UK.
Pantelis GeorgiouFaculty of Engineering, Department of Electrical and Electronic Engineering, Imperial College London, London, UK.
Timothy Miles RawsonCentres for Antimicrobial Optimisation Network, Imperial College London, London, UK.ORCID 0000-0002-2630-9722

Funding

UK Research and Innovation EP/Y030974/1Wellcome Trust
6 · The paper itself

Abstract

backgroundDevelopment of clinical decision support systems (CDSS) has been ongoing for over 60 years, more recently leveraging technologies such as artificial intelligence (AI) and machine learning (ML). Intelligent CDSS addressing different stages of the infection management process offer potential advantages in interpreting complex data and guiding clinical decision-making.

objectivesWe outline the current applications of AI-driven CDSS across the continuum of bacterial infection management, from prevention and diagnosis to antibiotic prescribing and treatment individualization. We discuss the main limitations hindering their translation into clinical practice, as well as opportunities to improve their development to better meet clinical needs.

methodsReferences for this review were identified through searches of PubMed, Google Scholar, bioRxiv and arXiv up to March 2025 by use of a combination of ML, decision-making and bacterial infection keywords. KEY

findingsAI-CDSS studies increasingly leverage multimodal electronic health record (EHR) data, with most adopting lower-complexity models that perform well on structured data, particularly when supported by effective feature engineering. Despite efforts to develop accurate AI-driven systems, some of which achieve clinician-level accuracy in solving diagnostic and prescribing tasks, AI-CDSS have largely failed to integrate into clinical settings. Their adoption faces challenges related to the narrow scope of the defined medical task, failure to consider stakeholder workflow and lack of proper evaluation frameworks.

conclusionThere is a need to shift CDSS development towards a more adaptive and holistic approach that recognizes the continuous nature of the decision-making process in infection management. Comprehensive AI-powered platforms that can model infection dynamics could improve antibiotic stewardship and help tackle the global health emergency of antimicrobial resistance.

Indexed as

Artificial IntelligenceBacterial InfectionsClinical Decision-MakingDecision Support Systems, ClinicalAnti-Bacterial AgentsHumansMachine LearningAnti-Bacterial Agents

Identifiers

PMID41424120
PMCPMC12802947

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.