ABSTRACT
Antimicrobial resistance (AMR) poses a critical global health challenge, necessitating innovative solutions for effective monitoring and healthcare diagnostics. Advances in artificial intelligence (AI) and machine learning (ML) offer transformative potential in combating AMR by enabling real-time surveillance, predictive modeling, and automated diagnostics. This study explores the latest AI-driven methodologies for AMR detection, resistance pattern prediction, and clinical decision support, focusing on their applications in precision medicine and public health interventions. The integration of AI with genomic sequencing, electronic health records (EHRs), and big data analytics enhances the early identification of resistant pathogens and optimizes antibiotic stewardship programs. Deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), play a pivotal role in analyzing vast microbiological datasets to detect resistance genes and predict antimicrobial susceptibility. Additionally, natural language processing (NLP) techniques facilitate the extraction of critical insights from unstructured clinical notes, improving diagnostic accuracy and treatment recommendations. This study also highlights federated learning frameworks that ensure privacy-preserving AMR data sharing across healthcare institutions while maintaining compliance with regulatory standards. AI-powered biosensors and automated laboratory systems further streamline pathogen identification, accelerating diagnosis and enabling timely interventions. Explainable AI (XAI) methodologies improve transparency in ML-driven AMR predictions, fostering trust among healthcare professionals and policymakers. Despite significant advancements, challenges remain, including data bias, ethical concerns, and the need for robust AI governance in AMR surveillance. Future research should focus on integrating AI with Internet of Things (IoT)-enabled diagnostic tools and blockchain technology for secure, real-time AMR data exchange. Expanding AI applications in low-resource settings can bridge healthcare disparities and strengthen global AMR mitigation efforts. By harnessing AI and ML for antimicrobial resistance monitoring and diagnostics, healthcare systems can enhance precision medicine, improve patient outcomes, and mitigate the growing threat of AMR. This study contributes to the ongoing discourse on AI-driven infectious disease management, advocating for interdisciplinary collaborations to refine AI frameworks for sustainable AMR control.
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