Table 3.

Characteristics of the selected items

Title and date	Author(s)	Main Contribution	Category
An accurate and dynamic predictive model for a smart M-Health system using machine learning [42] October 2020	Naseer Qreshi K, Din S., & Jeon G	This model is divided into data collection, data pre-processing, data partitioning, learning algorithm and the decision making for which it has been trained	M-Health
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review [32] October 2020	Lalmuanawma, S., Hussain, J., & Chhakchhuak, L	This report reviews existing information on the application of ML and AI to address the COVID-19 pandemic	COVID-19 screening, and management
Applications of Machine Learning Approaches in Emergency Medicine [36] June 2019	Shafaf N., & Malek H	This paper aims to compile and evaluate the existing studies in recent years on AI in EM, which can be categorised into different groups	Emergency medicine (EM)
Architecture of Smart Health Care System Using Artificial Intelligence [25] 2020	Kamruzzaman M. M	It is concluded that AI- or ML-based healthcare offers a multitude of improvements for the health sector	Pre-hospital medical care and disease screening
Artificial Intelligence and Machine Learning Applications in Musculoskeletal Imaging [43] February 2019	Enamandram S., Sandhu E., Bao H.Do, Reicher J., & Beaulieu CF	This article describes the key applications of supervised and unsupervised ML in musculoskeletal medicine. Such as diagnostic imaging, patient measurement data and clinical decision support	Others
Artificial Intelligence and Machine Learning in Emergency Medicine [34] July 2018	Stewart, J., Sprivulis, P., & Dwivedi, G	This article studies and conducts a research analysis of AI and ML in EM Finally, it is emphasised that, despite limitations, AI and its subfields are very useful as they can solve problems in a wide range of clinical domains	Emergency medicine (EM)
Artificial Intelligence for the Future Radiology Diagnostic Service [29] January 2021	Mun, S.K. Wong, K.H.,Lo, S.-C.B.,Li, Y., & Bayarsaikhan, S	In this chapter, artificial intelligence (AI) is explored along future lines in diagnostic radiology Three avenues are proposed for the important role of AI in radiology beyond current capabilities	Clinical decisions
Automatic Clinical Procedure Detection for Emergency Services [28] July 2019	Heard, J., Paris, R. A., Scully, D., McNaughton, C., Ehrenfeld, J. M., Coco, J., Fabbri, D., Bodenheimer, B., & Adams, J. A	A system based on human activity recognition algorithms to accurately recognise clinical processes and send data of these processes without the presence of the physician is evaluated	Clinical decisions
Classification of hospital admissions into emergency and elective care: a machine learning approach [39] November 2017	Krämer, J., Schreyögg, J., & Busse, R	This article focuses on the classification of hospital admissions in emergency care with a focus on ML	Medical services and/or emergency services
Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting [30] March 2021	Schwartz, J. M., Moy, A. J., Rossetti, S. C., Elhadad, N., & Cato, K. D	This article describes the involvement of clinicians in the development, evaluation and implementation of clinical decision support systems that use ML and analyse electronic medical record data to assist clinicians in their diagnosis and treatment, as well as in decision making	Clinical decisions
Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests [31] October 2020	Cabitza, F., Campagner, A.,Ferrari, D., Di Resta, C., Ceriotti, D., Sabetta, E., Colombini, A., De Vecchi, E., Banfi, G.,Locatelli, M., & Carobene, A	This paper studies the development and evaluation of machine learning models for the detection of COVID-19 based on blood tests The methodology carried out was to train 3 different datasets to develop different predictive models	COVID-19 screening, and management
Fall Detection for Elderly People using Machine Learning [37] July 2020	Badgujar S., & Pillai AS	This paper presents a fall detection system based on wearable sensors that are suitable for elderly people	Medical services and/or emergency services
IoT based healthcare monitoring system using 5G communication and Machine learning models [26] January 2021	Paramita, S., Bebartta, H. N. D., & Pattanayak, P	This smart system for patients by implanting wireless sensors in the body collects different vital aspects such as heart rate, blood pressure, etc	Pre-hospital medical care and disease screening
Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality? [40] November 2020	Alsuliman, T., Humaidan, D., & Sliman, L	This article arises because of the global trend towards digitisation of the healthcare system and how the need for it affects this area	Medical services and/or emergency services
Machine Learning for Predicting Emergency Incidents that Need an Air-ambulance [38] July 2020	Nuntalid N., & Richards D	The main objective of this article is to develop a real-time report to help the emergency medical service improve patient outcomes	Medical services and/or emergency services
Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review [27] February 2021	Muralitharan, S., Nelson, W., Di, S., McGillion, M., Devereaux, P., Barr, N. G., & Petch, J	The results obtained in this article are based on a systematic scoping review following the PRISMA-ScR model and conclude that the impact on ML-based early warning systems could be significant for clinicians and patients because of the decrease in false alerts and the increase in early detection	Clinical decisions
Machine Learning-based Risk of Hospital Readmissions: Predicting Acute Readmissions within 30 Days of Discharge [24] 2019	Baig, M. M., Hua, N., Zhang, E., Robinson, R., Armstrong, D., Whittaker, R., Robinson, T., Mirza, F., & Ullah, E	The proposed predictive model works better than other admission risk models. However, to further strengthen risk prediction and its clinical impact, the addition of non-clinical data such as social support is proposed as a future line of research	Pre-hospital medical care and disease screening
Review on machine and deep learning models for the detection and prediction of Coronavirus [33] June 2020	Ahmad W., Salehi Preety B., & Gaurav G	Solutions are proposed through AI by performing a scoping review following the PRISMA-ScR model The research concludes that so far there is no effective drug for the treatment of patients with COVID-19, but early detection or prediction of coronavirus cases may be possible with these predictive models	COVID-19 screening, and management
Role of machine learning in medical research: A survey [35] May 2020	Garg A., & Mago V	Different concepts of ML and DL and their possible medical application are studied and analysed	Emergency medicine (EM)
SaveMe: A Crime Deterrent Personal Safety Android App with a Bluetooth Connected Hardware Switch [41] August 2018	Tripti, N. F., Farhad, A., Iqbal, W., & Zaman, H. U	This application consists of a switch connected to the smartphone via Bluetooth that is pressed to alert the emergency contact of the victim in question of danger	M-Health