The Role of Artificial Intelligence in Antimicrobial Resistance Management: From Rapid Diagnosis to Novel Drug Discovery

Antimicrobial resistance (AMR) poses a significant global health challenge, necessitating innovative solutions for rapid diagnosis and effective management. Artificial intelligence (AI) has emerged as a transformative tool in combating AMR, leveraging machine learning (ML) and deep learning (DL) algorithms to enhance antibiotic susceptibility testing (AST), predict resistance patterns, and accelerate drug discovery. AI-driven automated systems, such as mobile applications for antibiogram analysis, achieve ۹۸% accuracy in classifying bacterial susceptibility, reducing human error and inter-operator variability. Integration with MALDI-TOF mass spectrometry enables rapid resistance profiling, while advanced models like Random Forest and convolutional neural networks (CNNs) analyze genomic and spectral data to identify rare resistance mechanisms. AI also optimizes antibiotic stewardship in intensive care units (ICUs) and pediatric populations, improving diagnostic precision and reducing unnecessary prescriptions. Despite its potential, challenges such as data quality, genetic diversity, and model generalizability persist. Future advancements require standardized databases, multimodal data integration, and clinician collaboration to fully harness AI's capabilities in AMR mitigation. By addressing these barriers, AI can revolutionize global health outcomes and pave the way for sustainable antimicrobial strategies.

کلیدواژه ها:

Antimicrobial Resistance (AMR) ، Artificial Intelligence (AI) ، Antibiotic Susceptibility Testing (AST) ، MALDI-TOF Mass Spectrometry ، Convolutional Neural Networks (CNNs)