ArticleHealth science reports2026
Artificial Intelligence in Rapid Diagnostics for Antimicrobial Resistance: From Bench to Bedside.
Article in Health science reports, 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
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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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Antimicrobial resistance (AMR) is one of the most urgent global public health threats, especially in low- and middle-income countries. In 2019, bacterial AMR was estimated to have directly caused approximately 1.27 million deaths and to have been associated with 4.95 million deaths worldwide. Rapid identification of resistant infections is important, yet established diagnostics are very slow. In recent years, artificial intelligence (AI) has revolutionized microbiology and infectious disease diagnostics. This review explores the space of AI-enabled diagnostics for AMR detection and characterization. Methods: This literature review was conducted from January to September 2026 using PubMed/MEDLINE, Google Scholar, and other electronic databases with keywords including "AI," "AMR," "deep learning," "genomic diagnostics," and "point-of-care (POC) testing." Following screening and full-text assessment, 67 Peer-reviewed articles and systematic reviews were included to support the synthesis presented in the manuscript, while non-peer-reviewed articles and studies lacking direct AI relevance were excluded. Titles and abstracts were screened before full-text review. Results: AI-enhanced diagnostics were reported to shorten turnaround time for AMR detection with promising but heterogeneous predictive performance; for example, pooled estimates across antimicrobial stewardship applications indicate an area under the curve of approximately 0.72. They also support improving antimicrobial stewardship and enable early therapeutic optimization in laboratory and POC settings, enabling real-time clinical decision-making. Despite these achievements, evidence for direct improvements in patient outcomes and routine bedside effectiveness remains limited. Conclusion: AI-driven diagnostic tools represent a promising strategy to bridge laboratory innovation and bedside application in combating AMR. The tools have the capacity to improve patient outcomes and enhance antimicrobial stewardship as they can accurately detect resistance patterns and support real-time clinical decisions. However, long-term sustainability, long-term success, and global impact can be achieved by continued validation, regulatory alignment, and equitable implementation across diverse healthcare settings.
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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.