ReviewFrontiers in cellular and infection microbiology2026
Artificial intelligence in optimizing antimicrobial therapy for gastro-renal disorders.
Review in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Differential Signature of Gut Microbiota in Female Patients with Condyloma Acuminatum and Its Association with Persistent Human Papillomavirus Infection.Journal of clinical medicine · 2026Article
- Integrative Multi-Omics Analysis Reveals Host Regulatory and Immune Networks with Inferred Dysbiosis Relevance in Colorectal Cancer.Journal of clinical medicine · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Antimicrobial therapy remains central to the management of gastrointestinal and urinary tract infections, yet its effectiveness is increasingly compromised by antimicrobial resistance and antibiotic-induced microbiome disruption. These challenges are particularly pronounced in gastro-renal settings, where recurrent infections, altered drug absorption and impaired renal clearance generate substantial pharmacokinetic variability and narrow therapeutic margins. Empiric, guideline-based regimens may therefore contribute to treatment failure, resistance selection, and disease recurrence. Artificial intelligence (AI) and machine learning offer novel opportunities to optimize antimicrobial therapy by integrating clinical, microbiological and multi-omics data to predict resistance, guide antibiotic selection and dosing, and support antimicrobial stewardship. However, clinical translation remains limited by data heterogeneity, insufficient prospective validation, regulatory constraints, and the need for continued human oversight. This review synthesizes current AI-driven strategies relevant to gastro-renal infections, highlighting shared pathophysiological challenges, practical clinical applications and key limitations. An integrated framework for AI-assisted antimicrobial optimization is proposed to enhance therapeutic efficacy while mitigating antimicrobial resistance and preserving microbiome integrity.
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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.