{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:55:41Z","timestamp":1792457741826,"version":"4.5.0"},"reference-count":160,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:p>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\u2013renal 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\u2013renal 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.<\/jats:p>","DOI":"10.3389\/fcimb.2026.1792361","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T14:46:43Z","timestamp":1779720403000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence in optimizing antimicrobial therapy for gastro-renal disorders"],"prefix":"10.3389","volume":"16","author":[{"given":"Oana","family":"Stoia","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Lucian Blaga University of Sibiu","place":["Sibiu, Romania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paula","family":"Anderco","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Lucian Blaga University of Sibiu","place":["Sibiu, Romania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teona","family":"Badiu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Lucian Blaga University of Sibiu","place":["Sibiu, Romania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel Bogdan","family":"Todor","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Lucian Blaga University of Sibiu","place":["Sibiu, Romania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristian","family":"Ichim","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Lucian Blaga University of Sibiu","place":["Sibiu, Romania"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"B1","article-title":"Ulcerative colitis: rapid evidence review","volume-title":"Am Fam Physician.","author":"Adams","year":"2022"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.glmedi.2024.100081","article-title":"Antimicrobial resistance: Impacts, challenges, and future prospects","volume":"2","author":"Ahmed","year":"2024","journal-title":"J. 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