The authors explore how artificial intelligence is being applied to infectious diseases, demonstrating how data-driven technologies can transform both early detection and therapeutic decision-making in conditions where time and accuracy are critical.
They review AI applications across multiple domains, including outbreak prediction, diagnostic imaging, microbiology, genomics, antimicrobial resistance detection, and clinical risk stratification. Machine-learning models are shown to analyze electronic health records, laboratory data, radiologic images, and pathogen genomic sequences to identify infection earlier, differentiate between viral and bacterial etiologies, and predict disease severity. The paper also highlights the role of AI in accelerating drug discovery, optimizing antimicrobial selection, and supporting antimicrobial stewardship programs by identifying patterns of resistance that may not be immediately apparent to clinicians.
From a public-health perspective, the implications are substantial. Infectious diseases remain a leading global cause of morbidity and mortality, and delayed diagnosis, inappropriate antibiotic use, and emerging resistance continue to strain health systems. AI-based detection tools can enable earlier isolation measures, faster initiation of targeted therapy, and more efficient allocation of limited resources, particularly during outbreaks or pandemics. At the treatment level, predictive models can help personalize antimicrobial therapy, reducing unnecessary broad-spectrum antibiotic exposure and slowing the development of resistance. The authors suggest that, when responsibly integrated into clinical workflows, these technologies could enhance surveillance capacity, strengthen preparedness for emerging pathogens, and support more precise, scalable, and equitable infectious-disease management.
Overall, the paper positions AI not merely as a diagnostic aid, but as a strategic public-health tool capable of reshaping how infectious diseases are detected, treated, and controlled.
