Improving the Precision of Etiological Diagnosis in Bacterial Infections Using Molecular Technologies: A Comparative Analysis of Platforms, AI Integration, and Point-of-Care Deployment

Bacterial infections impose a substantial global health burden, with antimicrobial resistance (AMR) further compounding the urgency of accurate and timely etiological diagnosis. Conventional culture-based methods, limited by extended turnaround times of 48–96 hours, reduced sensitivity in the presence of prior antibiotic exposure, and an inability to characterize resistomes at the molecular level, are progressively insufficient in the face of contemporary clinical demands. Molecular technologies have transformed bacteriological diagnostics by enabling rapid, sensitive, and highly specific pathogen identification directly from clinical specimens. Despite a growing body of primary evidence, no current review synthesizes these platforms under a unified comparative analytical framework that simultaneously addresses three critical dimensions: (1) the quantitative performance benchmarking of principal 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), across standardized parameters of sensitivity, specificity, limit of detection (LOD), time-to-result, and multiplexing capacity; (2) the integration of artificial intelligence and machine learning (AI/ML) algorithms into molecular diagnostic workflows for AMR prediction and clinical decision support; and (3) the translational trajectory of these technologies toward point-of-care (POC) deployment in decentralized and resource-limited settings. This review addresses this gap by providing a structured, evidence-based comparative analysis of molecular platforms applicable to bacterial infection diagnostics, critically evaluating their clinical validation status, AMR genotyping capabilities, AI augmentation potential, and readiness for POC implementation. We further delineate regulatory, health-economic, and implementation considerations, and identify key research priorities for the next generation of culture-independent precision bacteriological diagnostics.