Pennisi Flavia, Pinto Antonio, Ricciardi Giovanni Emanuele, Signorelli Carlo, Gianfredi Vincenza
PhD National Programme in One Health approaches to infectious diseases and life science research, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100, Pavia, Italy.
Faculty of Medicine, University Vita-Salute San Raffaele, Milan, Italy.
Eur J Clin Microbiol Infect Dis. 2025 Mar;44(3):463-513. doi: 10.1007/s10096-024-05027-y. Epub 2025 Jan 6.
The increasing threat of antimicrobial resistance has prompted a need for more effective antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) tools have emerged as potential solutions to enhance decision-making and improve patient outcomes in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and to assess its predictive performance and diagnostic accuracy. We conducted a comprehensive literature search across PubMed/MEDLINE, Scopus, EMBASE, and Web of Science to identify studies published up to July 2024. Studies included were observational, cohort, or retrospective, focusing on the application of AI/ML in AMS. The outcomes assessed were the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We calculated the mean pooled effect size (ES) and its 95% confidence interval (CI) using a random-effects model. The risk of bias was assessed using the QUADAS-AI tool, and the protocol was registered in PROSPERO. Out of 3,458 retrieved articles, 80 studies met the inclusion criteria. Our meta-analysis demonstrated that ML models exhibited strong predictive performance and diagnostic accuracy, with the following results: AUC [ES: 72.28 (70.42-74.14)], accuracy [ES: 74.97 (73.35-76.58)], sensitivity [ES: 76.89; (71.90-81.89)], specificity [ES: 73.77; (67.87-79.67)], NPV [ES:79.92 (76.54-83.31)], and PPV [ES: 69.41 (60.19-78.63)] across various AMS settings. AI and ML tools offer promising enhancements due to their strong predictive performance. The integration of AI into AMS could lead to more precise antimicrobial prescribing, reduced antimicrobial resistance, and better resource utilization.
抗菌药物耐药性威胁日益增加，促使人们需要更有效的抗菌药物管理计划（AMS）。人工智能（AI）和机器学习（ML）工具已成为增强AMS决策制定和改善患者预后的潜在解决方案。本系统评价和荟萃分析旨在评估AI在AMS中的影响，并评估其预测性能和诊断准确性。我们在PubMed/MEDLINE、Scopus、EMBASE和Web of Science上进行了全面的文献检索，以识别截至2024年7月发表的研究。纳入的研究为观察性、队列研究或回顾性研究，重点是AI/ML在AMS中的应用。评估的结果指标为曲线下面积（AUC）、准确性、敏感性、特异性、阴性预测值（NPV）和阳性预测值（PPV）。我们使用随机效应模型计算平均合并效应量（ES）及其95%置信区间（CI）。使用QUADAS-AI工具评估偏倚风险，研究方案已在PROSPERO中注册。在检索到的3458篇文章中，80项研究符合纳入标准。我们的荟萃分析表明，ML模型表现出强大的预测性能和诊断准确性，结果如下：在各种AMS环境中，AUC [ES：72.28（70.42 - 74.14）]、准确性[ES：74.97（73.35 - 76.58）]、敏感性[ES：76.89；（71.90 - 81.89）]、特异性[ES：73.77；（67.87 - 79.67）]、NPV [ES：79.92（76.54 - 83.31）]和PPV [ES：69.41（60.19 - 78.63）]。由于其强大的预测性能，AI和ML工具提供了有前景的改进。将AI整合到AMS中可导致更精确的抗菌药物处方、降低抗菌药物耐药性并更好地利用资源。
