Artificial Intelligence and Digital Systems for Infection Prevention in Hospital Care

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@article{1715679,
      author = {Moshood Ayinde, Glory Ohunyon, Prisca U Ojukwu},
      title = {Artificial Intelligence and Digital Systems for Infection Prevention in Hospital Care},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {8},
      pages = {2450-2472},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715679.pdf},
      abstract = {This study provides a comprehensive examination of the transformative role of artificial intelligence and digital systems in advancing infection prevention and control within modern healthcare environments. The purpose of the review is to synthesise current evidence on the conceptual foundations, technological applications, system integration, and implications for outcomes of AI-driven approaches to infection surveillance and management. A narrative review methodology was employed, drawing on interdisciplinary literature spanning clinical practice, public health, health informatics, and policy frameworks to ensure a holistic and critically informed analysis. The findings reveal that digital transformation is fundamentally reshaping infection prevention through the integration of predictive analytics, real-time surveillance systems, wearable technologies, and intelligent decision-support platforms. These innovations enable earlier detection of infectious threats, improved diagnostic precision, enhanced resource allocation, and more coordinated responses across healthcare systems. Furthermore, the incorporation of advanced computational approaches, including machine learning and emerging quantum-based models, demonstrates significant potential in supporting epidemic forecasting and policy simulation. Despite these advancements, the study identifies key challenges that constrain effective implementation, including data fragmentation, limited interoperability, ethical and governance concerns, and disparities in infrastructure and workforce readiness. These limitations highlight the need for more robust and context-sensitive implementation strategies that align technological capabilities with organisational and policy environments. The study concludes that AI and digital systems hold substantial promise for strengthening infection prevention; however, their impact is dependent on strategic integration, interdisciplinary collaboration, and sustained investment in capacity development. Recommendations include enhancing data 

interoperability, strengthening ethical and regulatory frameworks, investing in workforce digital competencies, and prioritising real-world validation of emerging technologies. These measures are essential for building resilient, adaptive healthcare systems capable of effectively responding to current and future infectious disease challenges.
},
      keywords = {Artificial intelligence; Infection prevention; Digital health systems; Healthcare surveillance; Predictive analytics; Health system resilience},
      month = {February},
      doi = {https://doi.org/10.64388/IREV9I8-1715679}
  }