Bacterial infections remain a major global health burden, further exacerbated by the rapid emergence of antimicrobial resistance (AMR), which increases the need for accurate and timely etiological diagnosis. Conventional culture-based methods are limited by prolonged turnaround times, reduced sensitivity in patients receiving prior antimicrobial therapy, and restricted ability to characterize resistance mechanisms at the molecular level. Molecular diagnostic technologies have significantly transformed bacteriological diagnostics by enabling rapid, sensitive, and specific pathogen detection directly from clinical specimens. This review provides a structured comparative analysis of major molecular platforms, including polymerase chain reaction (PCR) and its variants, isothermal amplification technologies, next-generation sequencing (NGS), clustered regularly interspaced short palindromic repeats (CRISPR) based diagnostics, and digital PCR (dPCR). Key analytical parameters such as sensitivity, specificity, limit of detection (LOD), time to result, and multiplexing capacity are evaluated to highlight platform-specific strengths and limitations. In addition, the integration of artificial intelligence and machine learning (AI/ML) into molecular diagnostic workflows for AMR prediction and clinical decision support is critically examined. The translational potential of these technologies toward point-of-care (POC) implementation is also discussed, with consideration of clinical validation, operational constraints, and real-world applicability. Overall, this review provides an integrated perspective on current molecular diagnostic strategies, emphasizing the balance between analytical performance and clinical interpretability, and outlines key challenges and future directions for advancing culture-independent bacteriological diagnostics.
Cotelici et al. (Wed,) studied this question.