Background: The ability to detect emerging antibiotic resistance trends in real-time is critical for infection control and public health planning. Traditional surveillance systems rely on retrospective data analysis, which often leads to delayed interventions and ineffective containment strategies. Artificial intelligence (AI) and deep learning offer transformative solutions for real-time monitoring, predictive analytics, and early warning systems for antimicrobial resistance (AMR). By integrating hospital data, whole-genome sequencing (WGS), and electronic health records (EHRs), AI can provide timely insights into resistance evolution and guide targeted interventions.

Methods: We developed an AI-powered epidemiological platform that utilizes long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and XGBoost models to analyze vast datasets from microbiology labs, hospital antibiograms, antimicrobial consumption trends, and clinical outcomes. The model applies natural language processing (NLP) to extract key resistance patterns from EHRs and integrates reinforcement learning algorithms to optimize antibiotic prescribing strategies.

Results: The AI-enhanced surveillance system successfully identified early warning signals for carbapenem-resistant Enterobacterales (CRE), vancomycin-resistant Enterococci (VRE), and multidrug-resistant Pseudomonas aeruginosa across multiple healthcare facilities. The predictive model enabled proactive infection control measures, reducing hospital-acquired infections by 20% and optimizing antibiotic stock management.

Conclusion: AI-driven surveillance platforms offer a paradigm shift in AMR monitoring, enabling rapid detection and mitigation strategies. Future research should focus on real-time AI model deployment, data standardization, and integration into hospital antimicrobial stewardship programs to maximize clinical impact.