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The rising burden of infectious diseases and the escalating threat of antimicrobial resistance (AMR) demand innovative solutions for global disease management. Artificial intelligence (AI) has emerged as a transformative tool, offering real-time surveillance, predictive modeling, personalized medicine, drug discovery, and vaccine development. This review examines the current applications, challenges, and future directions of AI in infectious disease control, with a focus on pathogen detection, outbreak prediction, personalized treatment, and the development of antimicrobial drugs. AI-driven machine learning (ML) and deep learning (DL) algorithms enable the early detection of diseases by analyzing large datasets from clinical records, genomic data, medical imaging, and epidemiological sources. AI-powered surveillance systems integrate data from social media, wearable devices, and environmental monitoring to forecast outbreaks and provide early warnings. AI also accelerates drug discovery and vaccine development through computational modeling and molecular simulations, cutting costs and development timelines. This review examines the pivotal role of AI in transforming infectious disease management by harnessing machine learning (ML), deep learning (DL), natural language processing (NLP), and bioinformatics-driven modeling. Despite its potential, AI adoption faces ethical, regulatory, and infrastructural barriers, including data privacy, bias, and access issues that must be addressed for transparent and inclusive healthcare. Collaborative efforts among AI researchers, clinicians, policymakers, and public health experts are vital to addressing these hurdles. Future efforts should focus on improving data sharing, integrating multi-omics, promoting global health equity, and implementing AI-driven One Health strategies. This review highlights recent advances, key challenges, and future directions for applying AI in anti-infective drug discovery and infectious disease management.
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Aeshah M. Mohammed
Mohammed Mohammed
Jawad K. Oleiwi
University of Malaya
Saveetha University
University of Baghdad
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Mohammed et al. (Wed,) studied this question.
www.synapsesocial.com/papers/6a1501576f520dd18d29e2ac — DOI: https://doi.org/10.1016/j.insi.2025.100118