A Systematic Review of the Factors Affecting the Artificial Intelligence Implementation in the Health Care Sector

Alhashmi, Shaikha F. S.; Alshurideh, Muhammad; Kurdi, Barween Al; Salloum, Said A.

doi:10.1007/978-3-030-44289-7_4

Cited by 99 publications

(31 citation statements)

References 42 publications

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“…Other scholars have investigated the use of mobile technologies and applications in student learning [4][5][6], mobile blended learning [7], enhancing learner participation and transforming pedagogy [8], to conduct student voting and the enhancement of engagement and participation [9]. Some scholars have looked at smartphone applications in the medical field [10,11] and those of engineering and technical education [12]. There is little investigation, however, into the use of the mobile phone as an accepted examination platform.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting the Use of Smart Mobile Examination Platforms by Universities’ Postgraduate Students during the COVID-19 Pandemic: An Empirical Study

et al. 2021

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Recent years have seen an increasingly widespread use of online learning technologies. This has prompted universities to make huge investments in technology to augment their position in the face of extensive competition and to enhance their students’ learning experience and efficiency. Numerous studies have been carried out regarding the use of online and mobile phone learning platforms. However, there are very few studies focusing on how university students will accept and adopt smartphones as a new platform for taking examinations. Many reasons, but most recently and importantly the COVID-19 pandemic, have prompted educational institutions to move toward using both online and mobile learning techniques. This study is a pioneer in examining the intention to use mobile exam platforms and the prerequisites of such intention. The purpose of this study is to expand the Technology Acceptance Model (TAM) by including four additional constructs: namely, content quality, service quality, information quality, and system quality. A self-survey method was prepared and carried out to obtain the necessary basic data. In total, 566 students from universities in the United Arab Emirates took part in this survey. Smart PLS was used to test the study constructs and the structural model. Results showed that all study hypotheses are supported and confirmed the effect of the TAM extension factors within the UAE higher education setting. These outcomes suggest that the policymakers and education developers should consider mobile exam platforms as a new assessment platform and a possible technological solution, especially when considering the distance learning concept. It is good to bear in mind that this study is initial and designed to explore using smartphones as a new platform for student examinations. Furthermore, mixed-method research is needed to check the effectiveness and the suitability of using the examination platforms, especially for postgraduate higher educational levels.

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Section: Introductionmentioning

confidence: 99%

Factors Affecting the Use of Smart Mobile Examination Platforms by Universities’ Postgraduate Students during the COVID-19 Pandemic: An Empirical Study

et al. 2021

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“…Inclusion quality assessment is one of the most essential and critical parts of any systematic review [35][36][37][38][39]. The assessment quality assurance checklist for our systematic review consists of 6 questions for the 16 chosen papers, as shown in Table 4.…”

Section: Quality Assessmentmentioning

confidence: 99%

A systematic review on sequence-to-sequence learning with neural network and its models

Yousuf

Lahzi

Salloum

et al. 2021

IJECE

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We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.

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“…Due to the large amounts of information during the past two decades, there emerged a need for technical methods of classifying huge texts as statistical and manual methods had never become useless in this field [35][36][37][38][39][40][41][42][43][44][45][46][47][48]. Therefore, machine learning classification techniques appeared which aim to classify unstructured texts and documents based on certain algorithms designed for this purpose.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…Algorithms of neural networks give very accurate results in the area of NLP and support deep learning very significantly as they solve some errors and problems related to the variance of data resulting from the process of deep learning [36][37][38][39]. Neural networks are used in the classification of texts to address linear and non-linear problems.…”

Section: Neural Networkmentioning

confidence: 99%

A systematic review of text classification research based on deep learning models in Arabic language

Wahdan

Hantoobi

Salloum

et al. 2020

IJECE

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Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.

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A Systematic Review of the Factors Affecting the Artificial Intelligence Implementation in the Health Care Sector

Cited by 99 publications

References 42 publications

Factors Affecting the Use of Smart Mobile Examination Platforms by Universities’ Postgraduate Students during the COVID-19 Pandemic: An Empirical Study

Factors Affecting the Use of Smart Mobile Examination Platforms by Universities’ Postgraduate Students during the COVID-19 Pandemic: An Empirical Study

A systematic review on sequence-to-sequence learning with neural network and its models

A systematic review of text classification research based on deep learning models in Arabic language

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