Over the past several decades, diagnostic microbiology has progressed from traditional culture methods to include modern, culture-independent molecular and metagenomic approaches for diagnosing infectious diseases and guiding antimicrobial therapy. Since the beginning of the twenty-first century, clinical diagnostic microbiology has made considerable strides in optimizing pathogen identification. This progress has been driven by the introduction of optimized sampling methods, advanced diagnostic kits, and new technologies like mass spectrometry for bacterial identification, real-time genomics, and adaptable culture systems. However, the costs of advanced molecular methods are very high, and they require massive instrumentation to reach a clinical diagnosis. Conventional cultures remain cost-effective and can be performed with minimal resource requirements compared to advanced laboratory equipment. However, the most significant challenge with conventional methods is the reporting time of results (several days). Since the newer molecular and genomic methods do not meet all the diagnostic demands, strategies have shifted toward employing techniques with higher precision, sensitivity, and better time efficiency. The integration of artificial intelligence and machine learning is set to redefine diagnostic paradigms, facilitating not only rapid and precise pathogen identification but also addressing foundational limitations in data analysis and interpretation. This review evaluates the synergy between conventional and emerging diagnostic technologies, emphasizing their clinical utility, limitations, and future trajectories for diverse audiences in microbiology and healthcare.
Goraya et al. (Wed,) studied this question.