Abstract

The rising antimicrobial resistance (AMR) among Klebsiella species presents the need for a transformative approach to understanding and combating the public health crisis crises with by integrating cutting-edge artificial intelligence (AI) in microbiological insights by employing machine learning techniques like deep learning, support vector machines and ensemble methods. Researchers can analyse and analyse vast datasets out discover intricate relationships between specific genomic features and resistance profiles. This synergy of microbiology also enhances it and informs targeted public health strategies and personalized treatment modalities. Various enigmas can like the challenges of data quality, interpretation and the need for robust validation frameworks to ensure the findings across diverse scientific contexts. Recent advancements have illuminated the genetic underpinnings of resistance to critical antibiotic classes, including carbapenems, cephalosporins, aminoglycosides, fluoroquinolones and tetracyclines. Ultimately, this review underscores the recent innovations in genomic data utilization utilisation, the potential of AI-driven approaches, and types of machine learning frameworks for the understanding Klebsiella AMR, fostering a proactive stance in antibiotic stewardship and improving human health where resistance is increasing humdrum.

Keywords

antibiotics, Klebsiella, AMR, machine learning, artificial intelligence