Kuo Kuang-Ming, Chang Chao Sheng
Department of Business Management, National United University, No. 1, Lienda, Miaoli, 360301, Taiwan.
Department of Emergency Medicine, E-Da Hospital, Kaohsiung City, Taiwan.
BMC Med Inform Decis Mak. 2025 May 15;25(1):187. doi: 10.1186/s12911-025-03010-x.
The rapid advancement of Artificial Intelligence (AI) has led to its widespread application across various domains, showing encouraging outcomes. Many studies have utilized AI to forecast emergency department (ED) disposition, aiming to forecast patient outcomes earlier and to allocate resources better; however, a dearth of comprehensive review literature exists to assess the objective performance standards of these predictive models using quantitative evaluations. This study aims to conduct a meta-analysis to assess the diagnostic accuracy of AI in predicting ED disposition, encompassing admission, critical care, and mortality.
Multiple databases, including Scopus, Springer, ScienceDirect, PubMed, Wiley, Sage, and Google Scholar, were searched until December 31, 2023, to gather relevant literature. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Pooled estimates of sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated to evaluate AI's predictive performance. Sub-group analyses were performed to explore covariates affecting AI predictive model performance.
The study included 88 articles possessed with 117 AI models, among which 39, 45, and 33 models predicted admission, critical care, and mortality, respectively. The reported statistics for sensitivity, specificity, and AUROC represent pooled summary measures derived from the component studies included in this meta-analysis. AI's summary sensitivity, specificity, and AUROC for predicting admission were 0.81 (95% Confidence Interval [CI] 0.74-0.86), 0.87 (95% CI 0.81-0.91), and 0.87 (95% CI 0.84-0.93), respectively. For critical care, the values were 0.86 (95% CI 0.79-0.91), 0.89 (95% CI 0.83-0.93), and 0.93 (95% CI 0.89-0.95), respectively, and for mortality, they were 0.85 (95% CI 0.80-0.89), 0.94 (95% CI 0.90-0.96), and 0.93 (95% CI 0.89-0.96), respectively. Emergent sample characteristics and AI techniques showed evidence of significant covariates influencing the heterogeneity of AI predictive models for ED disposition.
The meta-analysis indicates promising performance of AI in predicting ED disposition, with certain potential for improvement, especially in sensitivity. Future research could explore advanced AI techniques such as ensemble learning and cross-validation with hyper-parameter tuning to enhance predictive model efficacy.
This systematic review was not registered with PROSPERO or any other similar registry because the review was completed prior to the opportunity for registration, and PROSPERO currently does not accept registrations for reviews that are already completed. We are committed to transparency and have adhered to best practices in systematic review methodology throughout this study.
人工智能（AI）的迅速发展使其在各个领域得到广泛应用，并取得了令人鼓舞的成果。许多研究利用人工智能来预测急诊科（ED）的处置情况，旨在更早地预测患者的结局并更好地分配资源；然而，缺乏全面的综述文献来使用定量评估来评估这些预测模型的客观性能标准。本研究旨在进行一项荟萃分析，以评估人工智能在预测急诊科处置情况（包括入院、重症监护和死亡率）方面的诊断准确性。
检索了多个数据库，包括Scopus、Springer、ScienceDirect、PubMed、Wiley、Sage和谷歌学术，直至2023年12月31日，以收集相关文献。使用预测模型偏倚风险评估工具评估偏倚风险。计算敏感性、特异性和受试者工作特征曲线下面积（AUROC）的合并估计值，以评估人工智能的预测性能。进行亚组分析以探索影响人工智能预测模型性能的协变量。
该研究纳入了88篇文章，其中包含117个人工智能模型，其中39、45和33个模型分别预测入院、重症监护和死亡率。报告的敏感性、特异性和AUROC统计数据代表了从该荟萃分析中纳入的各组成研究得出的合并汇总指标。人工智能预测入院的汇总敏感性、特异性和AUROC分别为0.81（95%置信区间[CI]0.74 - 0.86）、0.87（95%CI 0.81 - 0.91）和0.87（95%CI 0.84 - 0.93）。对于重症监护，相应的值分别为0.86（95%CI 0.79 - 0.91）、0.89（95%CI 0.83 - 0.93）和0.93（95%CI 0.89 - 0.95），对于死亡率，分别为0.85（95%CI 0.80 - 0.89）、0.94（95%CI 0.90 - 0.96）和0.93（95%CI 0.89 - 0.96）。紧急样本特征和人工智能技术显示出有显著协变量影响急诊科处置人工智能预测模型异质性的证据。
荟萃分析表明人工智能在预测急诊科处置情况方面具有良好的性能，但仍有一定的改进潜力，尤其是在敏感性方面。未来的研究可以探索先进的人工智能技术，如集成学习和带有超参数调整的交叉验证，以提高预测模型的效能。
本系统评价未在PROSPERO或任何其他类似注册机构注册，因为该评价在有机会注册之前就已完成且PROSPERO目前不接受已完成评价的注册。我们致力于透明度，并在本研究的整个过程中坚持系统评价方法的最佳实践。
