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<p>Antimicrobial resistance (AMR) has reached a critical inflection point, with the World Health Organization projecting that drug-resistant infections could claim 10&#xa0;million lives annually by 2050 if left unaddressed. Central to combating AMR is antimicrobial susceptibility testing (AST), which guides rational antibiotic prescribing, yet conventional phenotypic and genotypic methods remain fundamentally constrained by prolonged turnaround times (16–72&#xa0;h), dependency on pure culture isolation, high infrastructure costs, and the inability to capture complex in vivo resistance dynamics. These limitations perpetuate empirical antibiotic prescribing, with 30–50% of prescriptions estimated to be inappropriate, accelerating the emergence and spread of resistance. This review critically evaluates the full landscape of AST diagnostics from FDA-cleared commercial platforms (phenotypic and genotypic) to a new generation of emerging technologies, including microfluidic chip-based systems, lateral flow immunoassays, CRISPR-Cas biosensors, electrochemical sensors, and metagenomic/metatranscriptomic approaches. Emphasis is placed on artificial intelligence and machine learning (AI/ML)-integrated platforms, which have demonstrated &gt; 90% susceptibility prediction accuracy and the capacity to deliver results within minutes to hours without requiring bacterial culture. This review provides a systematic bridge between commercialized diagnostics and emerging research-stage platforms, offering a comparative analysis of sensitivity, turnaround time, clinical feasibility, and regulatory readiness.</p>

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Rapid antimicrobial susceptibility testing: bridging commercial platforms and emerging technologies toward clinical implementation

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Pankaj Kumar,
  • Huisoo Jang,
  • Hyojeong Lee,
  • Junhyeon Jeon,
  • Sun Min Kim,
  • Tae-Joon Jeon

摘要

Antimicrobial resistance (AMR) has reached a critical inflection point, with the World Health Organization projecting that drug-resistant infections could claim 10 million lives annually by 2050 if left unaddressed. Central to combating AMR is antimicrobial susceptibility testing (AST), which guides rational antibiotic prescribing, yet conventional phenotypic and genotypic methods remain fundamentally constrained by prolonged turnaround times (16–72 h), dependency on pure culture isolation, high infrastructure costs, and the inability to capture complex in vivo resistance dynamics. These limitations perpetuate empirical antibiotic prescribing, with 30–50% of prescriptions estimated to be inappropriate, accelerating the emergence and spread of resistance. This review critically evaluates the full landscape of AST diagnostics from FDA-cleared commercial platforms (phenotypic and genotypic) to a new generation of emerging technologies, including microfluidic chip-based systems, lateral flow immunoassays, CRISPR-Cas biosensors, electrochemical sensors, and metagenomic/metatranscriptomic approaches. Emphasis is placed on artificial intelligence and machine learning (AI/ML)-integrated platforms, which have demonstrated > 90% susceptibility prediction accuracy and the capacity to deliver results within minutes to hours without requiring bacterial culture. This review provides a systematic bridge between commercialized diagnostics and emerging research-stage platforms, offering a comparative analysis of sensitivity, turnaround time, clinical feasibility, and regulatory readiness.