Abstract
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.
Highlights
IntroductionPeople are familiar with artificial intelligence and machine learning. These terms are beginning to be present in all facets of the world, whether consciously or unconsciously, as they are linked to computer processes and automated artificial intelligence systems [1].1 3 Vol.:(0123456789) 88 Page 2 of 16Journal of Medical Systems (2021) 45: 88Artificial intelligence is a field that is composed of computer science methods and reliable datasets
Nowadays, people are familiar with artificial intelligence and machine learning
31 articles were found for content analysis during the eligibility stage and 11 of them were subsequently eliminated because the information they contained did not focus on healthcare emergencies
Summary
People are familiar with artificial intelligence and machine learning. These terms are beginning to be present in all facets of the world, whether consciously or unconsciously, as they are linked to computer processes and automated artificial intelligence systems [1].1 3 Vol.:(0123456789) 88 Page 2 of 16Journal of Medical Systems (2021) 45: 88Artificial intelligence is a field that is composed of computer science methods and reliable datasets. People are familiar with artificial intelligence and machine learning. These terms are beginning to be present in all facets of the world, whether consciously or unconsciously, as they are linked to computer processes and automated artificial intelligence systems [1]. The objective of artificial intelligence is to provide novel and effective methods for problem-solving. It encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms that seek to create expert systems that make predictions or classifications based on input data. The complexity of artificial intelligence systems ranges from simple methods direction for specific tasks to abstract methods that aim to imitate human intelligence using computers
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