ReviewDigital health
Role of artificial intelligence in bacterial diagnostics and surveillance of anti-microbial resistance.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Rapid diagnostics innovations for urinary tract infections using molecular biology, artificial intelligence and antimicrobial resistance surveillance: a comprehensive review.Molecular biology reports · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) is a serious global health threat caused by the overuse and misuse of antibiotic resulting in treatment failure. The current conventional techniques have various constraints requiring specialized expertise, longer turnaround times requiring rapid point of care and transformative solutions. This narrative review explores the applications of artificial intelligence (AI) in AMR diagnostics. A structured search of PubMed, Web of Science, and Google Scholar was conducted using the MeSH terms. Relevant studies were screened and synthesized in four themes; phenotypic and genotypic identification, antimicrobial susceptibility testing (AST), AMR surveillance, and antibiotic development. The reporting was guided by the Scale for the Assessment of Narrative Review Articles (SANRA). Across AMR diagnostic, machine learning and deep learning improved the accuracy, reproducibility, and scalability of bacterial identification by learning complex patterns of AMR. The AI models utilized wide variety of data including genomic profiles, radiological imaging, microscopy, agar plate photography, and biochemical signatures such as MALDI TOF mass spectrometry. In Antibiotic Susceptibility Tests (AST), AI helped in standardizing the interpretation of disc diffusion and MIC assays. In AMR surveillance, AI models supported screening and genomic detection of resistance determinants, which enabled the identification of resistance trends and policy evaluations of One Health integration. In antibiotic development, AI contributed to therapeutic discoveries through screening large chemical libraries and designing antimicrobial peptides or adjuvants with reduced experimental burden. The reviewed evidence indicates that AI substantially enhances decision making in scope of AMR challenges. Effectively continued impact will depend on data quality, model development and integration into public health and laboratory infrastructures.
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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.