{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T18:24:17Z","timestamp":1787250257322,"version":"build-2736575974"},"reference-count":24,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Introduction<\/jats:title>\n Antimicrobial resistance (AMR) remains a major global health concern, necessitating timely and accurate diagnostic approaches to guide appropriate therapy. Conventional antibiotic susceptibility testing (AST) is often associated with delays that may compromise clinical outcomes.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To evaluate the impact of integrating rapid diagnostic testing, genotypic resistance profiling, and artificial intelligence (AI)-based prediction models on antimicrobial stewardship and clinical outcomes.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A prospective observational study was conducted at Lady Reading Hospital, MTI, Peshawar, Pakistan from 13\/02\/2023\u201318\/04\/2025, including 410 adult patients with suspected bacterial infections. Participants underwent conventional AST, rapid diagnostic testing, and molecular detection of resistance genes. An AI-based model was developed using clinical and laboratory parameters to predict antimicrobial resistance. Key outcomes included time to effective therapy, antibiotic appropriateness, length of hospital stay, and mortality. Statistical analysis included comparative tests, logistic regression, and receiver operating characteristic (ROC) curve analysis and was performed using SPSS Version 26.0 and R version 4.5.2.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Rapid diagnostics significantly reduced time to pathogen identification (10.4 vs 48.6 hours, p\u2009<\u20090.001) and initiation of effective therapy (20.3 vs 55.8 hours, p\u2009<\u20090.001). Appropriate antibiotic use improved from 54.2% to 78.3% (p\u2009<\u20090.001), while broad-spectrum antibiotic use declined significantly. The integrated approach was associated with reduced hospital stay (8.6 vs 12.9 days, p\u2009<\u20090.001) and lower mortality (14.0% vs 22.7%, p\u2009=\u20090.02). Genotypic\u2013phenotypic concordance was substantial (\u03ba\u2009=\u20090.61\u20130.69). The AI model demonstrated strong predictive performance (AUC\u2009=\u20090.89).<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n An integrated diagnostic and predictive approach was associated with improvements in antimicrobial stewardship and favorable clinical outcomes, suggesting its potential as a cost-effective and scalable strategy for addressing antimicrobial resistance (AMR), particularly in resource-limited settings.<\/jats:p>\n <\/jats:sec>","DOI":"10.1371\/journal.pone.0347223","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T17:29:54Z","timestamp":1787246994000},"page":"e0347223","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrated evaluation of rapid diagnostic testing, genotypic-phenotypic resistance profiling, and AI-Assisted prediction models for antimicrobial stewardship and clinical outcomes in a resource-limited setting"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8894-3692","authenticated-orcid":true,"given":"Muhammad","family":"Hammad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rasikh","family":"Arif","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sadaf","family":"Fardoos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khadija","family":"Shakoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Nasir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"issue":"10325","key":"pone.0347223.ref001","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/S0140-6736(21)02724-0","article-title":"Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis","volume":"399","author":"CJ Murray","year":"2022","journal-title":"The Lancet"},{"issue":"17","key":"pone.0347223.ref002","doi-asserted-by":"crossref","first-page":"2244","DOI":"10.3390\/diagnostics15172244","article-title":"Diagnostic Innovations to Combat Antibiotic Resistance in Critical Care: Tools for Targeted Therapy and Stewardship","volume":"15","author":"AD Alatawi","year":"2025","journal-title":"Diagnostics (Basel)"},{"issue":"14","key":"pone.0347223.ref003","first-page":"1093","article-title":"Antibiotic susceptibility testing for therapy and antimicrobial resistance surveillance: genotype beats phenotype?. 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