Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47.011, Valladolid, Spain.
Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal.
J Med Syst. 2021 Aug 18;45(10):88. doi: 10.1007/s10916-021-01762-3.
Despite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.
尽管医学领域对人工智能研究的需求不断增加，但他的方法在卫生应急中的功能仍不清楚。因此，作者进行了这项系统评价和全球概述研究，旨在识别、分析和评估不同平台上的可用研究及其在医疗保健应急中的应用。用于识别和选择科学研究以及不同应用的方法包括两种。一方面，在 Google Scholar、IEEE Xplore、PubMed ScienceDirect 和 Scopus 中应用了 PRISMA 方法。另一方面，在最知名的商业平台（Android 和 iOS）中审查了商业应用程序。这项综述共纳入了 20 项研究。纳入的研究大多为临床决策（n=4，20%）或医疗服务或急救服务（n=4，20%）。只有 2 项研究关注移动医疗（n=2，10%）。另一方面，选择了 12 个应用程序在不同设备上进行全面测试。这些应用程序涉及院前医疗护理（n=3，25%）或临床决策支持（n=3，25%）。总的来说，这些应用程序中有一半基于基于自然语言处理的机器学习。机器学习在医疗保健领域的应用越来越广泛，为提高医疗保健的效率和质量提供了解决方案。随着移动健康设备和应用程序的出现，可以使用数据并评估患者的实时健康状况，机器学习是医疗保健行业的一个新兴趋势。
