El Arab Rabie Adel, Almoosa Zainab, Alkhunaizi May, Abuadas Fuad H, Somerville Joel
Almoosa College of Health Sciences, Al Mubarraz, Saudi Arabia.
Department of Infectious Disease, Almoosa Specialist Hospital, Al Mubarraz, Saudi Arabia.
Front Public Health. 2025 Apr 2;13:1547450. doi: 10.3389/fpubh.2025.1547450. eCollection 2025.
Hospital-acquired infections (HAIs) represent a persistent challenge in healthcare, contributing to substantial morbidity, mortality, and economic burden. Artificial intelligence (AI) offers promising potential for improving HAIs prevention through advanced predictive capabilities.
To evaluate the effectiveness, usability, and challenges of AI models in preventing, detecting, and managing HAIs.
This integrative review synthesized findings from 42 studies, guided by the SPIDER framework for inclusion criteria. We assessed the quality of included studies by applying the TRIPOD checklist to individual predictive studies and the AMSTAR 2 tool for reviews.
AI models demonstrated high predictive accuracy for the detection, surveillance, and prevention of multiple HAIs, with models for surgical site infections and urinary tract infections frequently achieving area-under-the-curve (AUC) scores exceeding 0.80, indicating strong reliability. Comparative data suggest that while both machine learning and deep learning approaches perform well, some deep learning models may offer slight advantages in complex data environments. Advanced algorithms, including neural networks, decision trees, and random forests, significantly improved detection rates when integrated with EHRs, enabling real-time surveillance and timely interventions. In resource-constrained settings, non-real-time AI models utilizing historical EHR data showed considerable scalability, facilitating broader implementation in infection surveillance and control. AI-supported surveillance systems outperformed traditional methods in accurately identifying infection rates and enhancing compliance with hand hygiene protocols. Furthermore, Explainable AI (XAI) frameworks and interpretability tools such as Shapley additive explanations (SHAP) values increased clinician trust and facilitated actionable insights. AI also played a pivotal role in antimicrobial stewardship by predicting the emergence of multidrug-resistant organisms and guiding optimal antibiotic usage, thereby reducing reliance on second-line treatments. However, challenges including the need for comprehensive clinician training, high integration costs, and ensuring compatibility with existing workflows were identified as barriers to widespread adoption.
The integration of AI in HAI prevention and management represents a potentially transformative shift in enhancing predictive capabilities and supporting effective infection control measures. Successful implementation necessitates standardized validation protocols, transparent data reporting, and the development of user-friendly interfaces to ensure seamless adoption by healthcare professionals. Variability in data sources and model validations across studies underscores the necessity for multicenter collaborations and external validations to ensure consistent performance across diverse healthcare environments. Innovations in non-real-time AI frameworks offer viable solutions for scaling AI applications in low- and middle-income countries (LMICs), addressing the higher prevalence of HAIs in these regions.
Artificial Intelligence stands as a transformative tool in the fight against hospital-acquired infections, offering advanced solutions for prevention, surveillance, and management. To fully realize its potential, the healthcare sector must prioritize rigorous validation standards, comprehensive data quality reporting, and the incorporation of interpretability tools to build clinician confidence. By adopting scalable AI models and fostering interdisciplinary collaborations, healthcare systems can overcome existing barriers, integrating AI seamlessly into infection control policies and ultimately enhancing patient safety and care quality. Further research is needed to evaluate cost-effectiveness, real-world applications, and strategies (e.g., clinician training and the integration of explainable AI) to improve trust and broaden clinical adoption.
医院获得性感染（HAIs）是医疗保健领域持续面临的挑战，会导致严重的发病率、死亡率和经济负担。人工智能（AI）通过先进的预测能力，为改善医院获得性感染的预防提供了广阔前景。
评估人工智能模型在预防、检测和管理医院获得性感染方面的有效性、可用性和挑战。
本整合性综述综合了42项研究的结果，纳入标准以SPIDER框架为指导。我们通过将TRIPOD清单应用于个体预测性研究，并使用AMSTAR 2工具进行综述，来评估纳入研究的质量。
人工智能模型在检测、监测和预防多种医院获得性感染方面显示出较高的预测准确性，手术部位感染和尿路感染模型的曲线下面积（AUC）得分经常超过0.80，表明可靠性很强。比较数据表明，虽然机器学习和深度学习方法都表现良好，但一些深度学习模型在复杂数据环境中可能具有轻微优势。包括神经网络、决策树和随机森林在内的先进算法，与电子健康记录（EHRs）集成时显著提高了检测率，实现了实时监测和及时干预。在资源有限的环境中，利用历史电子健康记录数据的非实时人工智能模型显示出相当大的可扩展性，便于在感染监测和控制中更广泛地实施。人工智能支持的监测系统在准确识别感染率和提高手卫生规范的依从性方面优于传统方法。此外，可解释人工智能（XAI）框架和诸如夏普利值（SHAP）等可解释性工具增加了临床医生的信任，并促进了可采取行动的见解。人工智能在抗菌药物管理中也发挥了关键作用，通过预测多重耐药菌的出现并指导最佳抗生素使用，从而减少对二线治疗的依赖。然而，包括需要全面的临床医生培训、高集成成本以及确保与现有工作流程的兼容性等挑战，被确定为广泛采用的障碍。
人工智能在医院获得性感染预防和管理中的整合，代表了在增强预测能力和支持有效感染控制措施方面潜在的变革性转变。成功实施需要标准化的验证方案、透明的数据报告以及开发用户友好的界面，以确保医疗保健专业人员无缝采用。各研究中数据源和模型验证的差异强调了多中心合作和外部验证的必要性，以确保在不同医疗环境中的一致性能。非实时人工智能框架的创新为在低收入和中等收入国家（LMICs）扩大人工智能应用提供了可行的解决方案，这些地区医院获得性感染的患病率较高。
人工智能是对抗医院获得性感染的变革性工具，为预防、监测和管理提供了先进的解决方案。为了充分发挥其潜力，医疗保健部门必须优先考虑严格的验证标准、全面的数据质量报告以及纳入可解释性工具以建立临床医生的信心。通过采用可扩展的人工智能模型并促进跨学科合作，医疗保健系统可以克服现有障碍，将人工智能无缝集成到感染控制政策中，并最终提高患者安全和护理质量。需要进一步研究来评估成本效益、实际应用以及策略（例如临床医生培训和可解释人工智能的整合），以提高信任度并扩大临床应用。
