Artificial Intelligence for Antimicrobial Resistance Detection and Prediction in Klebsiella pneumoniae: A Systematic Review of Clinical Microbiology Applications.

BACKGROUND

Klebsiella pneumoniae is a WHO critical-priority pathogen associated with a substantial antimicrobial resistance (AMR) burden. Conventional microbiology workflows, including antimicrobial susceptibility testing, often require 36-72 hours, prolonging empirical therapy and contributing to antibiotic overuse. Artificial intelligence (AI) has emerged as a promising approach for enhancing the detection and prediction of antimicrobial resistance.

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 K. pneumoniae. Studies without K. pneumoniae-specific extractable outcomes were excluded. Data on study characteristics, input modalities, AI methods, performance, workflow gains, and validation methods were extracted and narratively synthesised. Risk of bias was assessed using PROBAST and QUADAS-2 according to study design.

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 K. pneumoniae is advancing rapidly, with MALDI-TOF-enabled approaches appearing most readily translatable to clinical microbiology workflows. However, the field remains dominated by retrospective, internally validated studies, often using imperfect automated susceptibility systems as reference standards. Progress now depends on rigorous external and prospective multicentre validation using geographically diverse datasets.