Ponsiglione Alfonso Maria, Zaffino Paolo, Ricciardi Carlo, Di Laura Danilo, Spadea Maria Francesca, De Tommasi Gianmaria, Improta Giovanni, Romano Maria, Amato Francesco
Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Naples 80125, Italy.
Department of Clinical and Experimental Medicine, University 'Magna Graecia' of Catanzaro, Catanzaro 88100, Italy.
Prog Biomed Eng (Bristol). 2024 Feb 9;6(2). doi: 10.1088/2516-1091/ad225a.
Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and AI could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and AI approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and AI as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed AI strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligentmodels of healthcare processes and to provide effective translation to the clinics.
仿真模型和人工智能（AI）在很大程度上被用于解决医疗保健和生物医学工程问题。这两种方法在医疗保健流程的分析和优化方面都显示出了有前景的结果。因此，仿真模型和人工智能的结合可以提供一种进一步提高医疗服务质量的策略。在这项工作中，对应用混合仿真模型和人工智能方法来应对医疗管理挑战的研究进行了系统综述。独立评审人员对Scopus、科学网和PubMed数据库进行了筛选。确定并讨论了将仿真和人工智能相结合的主要策略以及主要的医疗应用场景。此外，还描述了实施所提出方法的工具和算法。结果表明，机器学习似乎是与仿真模型相结合时最常用的人工智能策略，仿真模型主要依赖基于智能体的系统和离散事件系统。纳入研究的稀缺性和异质性表明，在医疗管理中实施混合机器学习 - 仿真方法的标准化框架尚未确定。未来的努力应旨在利用这些方法设计新型的医疗保健流程智能模型，并有效地转化应用于临床。
