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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\u00a0carried out in <jats:italic>Google Scholar<\/jats:italic>, <jats:italic>IEEE Xplore<\/jats:italic>, <jats:italic>PubMed ScienceDirect,<\/jats:italic> and <jats:italic>Scopus<\/jats:italic>. 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 (<jats:italic>n<\/jats:italic>\u2009=\u20094, 20%) or medical services or emergency services (<jats:italic>n<\/jats:italic>\u2009=\u20094, 20%). Only 2 were focused on m-health (<jats:italic>n<\/jats:italic>\u2009=\u20092, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (<jats:italic>n<\/jats:italic>\u2009=\u20093, 25%) or clinical decision support (<jats:italic>n<\/jats:italic>\u2009=\u20093, 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.<\/jats:p>","DOI":"10.1007\/s10916-021-01762-3","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T05:24:20Z","timestamp":1629350660000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Machine Learning in Medical Emergencies: a Systematic Review and Analysis"],"prefix":"10.1007","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2856-6044","authenticated-orcid":false,"given":"In\u00e9s Robles","family":"Mendo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5834-6571","authenticated-orcid":false,"given":"Gon\u00e7alo","family":"Marques","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3134-7720","authenticated-orcid":false,"given":"Isabel","family":"de la Torre D\u00edez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1543-9732","authenticated-orcid":false,"given":"Miguel","family":"L\u00f3pez-Coronado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1773-2860","authenticated-orcid":false,"given":"Francisco","family":"Mart\u00edn-Rodr\u00edguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"1762_CR1","unstructured":"Riedl, M.O.: Human\u2010centered artificial intelligence and machine learning. 1, (2009)."},{"key":"1762_CR2","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1093\/cid\/cix731","volume":"66","author":"J Wiens","year":"2018","unstructured":"Wiens, J., Shenoy, E.S.: Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology. 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