Rapid diagnostics innovations for urinary tract infections using molecular biology, artificial intelligence and antimicrobial resistance surveillance: a comprehensive review.

Authors

Soniya Goyal, Pooja Sharma, Deepak Aggarwal, Sameer Singh Faujdar

Published in

Molecular biology reports. Volume 53. Issue 1. Jul 15, 2026. Epub Jul 15, 2026.

Abstract

Urinary tract infections (UTIs) are among the most prevalent bacterial infections worldwide, accounting for a major clinical impact due to high recurrence rates, microbial diversity, biofilm formation, polymicrobial contribution and the rapid emergence of antimicrobial resistance (AMR). Traditional culture-based diagnostic approaches are constrained by high turnaround times and insufficient resolution of virulence and resistance factors, frequently result in empirical antibiotic therapy. Recent breakthroughs in molecular biology and biotechnology have fuelled the advancement of innovative diagnostic approaches for the quick, sensitive and pathogen-specific detection of uro-pathogens. Rapid pathogen identification and antibiotic susceptibility testing are critically needed for the effective targeted antibiotic therapy. This paper initially examines promising technologies employing machine learning models to provide rapid diagnostic outcomes, specifically for urinary tract infections. This paper focuses on promising molecular diagnostic technologies such as nucleic acid amplification, biosensor-based platforms, microfluidic lab-on-chip systems and omics-based approaches, along with their amalgamation with Artificial intelligence (AI) and smart diagnostics. Improved diagnostic precision, recommendations for targeted antimicrobial therapy and support for antimicrobial resistance surveillance and management are among the translational applications of these developments that are highlighted. Challenges related to medical ethics, execution and regulations are also covered. The combined efforts of next-generation and AI-assisted diagnostic tools offer a revolutionary paradigm for accurate and efficient antibiotic resistance control to treat UTIs.

PMID:
42455367
Bibliographic data and abstract were imported from PubMed on 15 Jul 2026.

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