ReviewInfection and drug resistance2026
Artificial Intelligence for Antimicrobial Resistance Detection and Prediction in
Review in Infection and drug resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Methods: We searched four databases (PubMed, EMBASE, MEDLINE, and CENTRAL) from 1 January 2010 to 3 January 2026 for peer-reviewed, original research studies evaluating AI methods for the detection and/or prediction of AMR in Results: Fifty-seven studies were included, with publication output accelerating sharply in 2024-2025 (27/57, 47.4%). Most studies originated from East Asia and predominantly aimed to classify resistance phenotypes from pre-AST data (37/57, 64.9%) using machine learning approaches. MALDI-TOF mass spectrometry was the most common input modality (27/57, 47.4%), followed by genomic sequencing and vibrational spectroscopy (12/57 each, 21.1%). Random forests were the most frequently studied model family (28/57, 49.1%), with high reported discrimination. Among AUROC/AUC-primary studies, 26/35 (74.3%) reported best-model performance ≥0.90; however, overall risk of bias was high, present in 45/57 studies (78.9%). Internally validated study designs predominated, with external validation reported in only 17/57 studies (29.8%), and prospective, real-world evaluation in 1/57. Conclusion: AI-based AMR prediction and detection in
Indexed as
Identifiers
What OpenQuestion holds
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.