Literature DB >> 32406857

Health Care Employees' Perceptions of the Use of Artificial Intelligence Applications: Survey Study.

Rana Abdullah1, Bahjat Fakieh1.   

Abstract

BACKGROUND: The advancement of health care information technology and the emergence of artificial intelligence has yielded tools to improve the quality of various health care processes. Few studies have investigated employee perceptions of artificial intelligence implementation in Saudi Arabia and the Arabian world. In addition, limited studies investigated the effect of employee knowledge and job title on the perception of artificial intelligence implementation in the workplace.
OBJECTIVE: The aim of this study was to explore health care employee perceptions and attitudes toward the implementation of artificial intelligence technologies in health care institutions in Saudi Arabia.
METHODS: An online questionnaire was published, and responses were collected from 250 employees, including doctors, nurses, and technicians at 4 of the largest hospitals in Riyadh, Saudi Arabia.
RESULTS: The results of this study showed that 3.11 of 4 respondents feared artificial intelligence would replace employees and had a general lack of knowledge regarding artificial intelligence. In addition, most respondents were unaware of the advantages and most common challenges to artificial intelligence applications in the health sector, indicating a need for training. The results also showed that technicians were the most frequently impacted by artificial intelligence applications due to the nature of their jobs, which do not require much direct human interaction.
CONCLUSIONS: The Saudi health care sector presents an advantageous market potential that should be attractive to researchers and developers of artificial intelligence solutions. ©Rana Abdullah, Bahjat Fakieh. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 14.05.2020.

Entities:  

Keywords:  Saudi Arabia; artificial intelligence; employees; healthcare sector; perception

Year:  2020        PMID: 32406857     DOI: 10.2196/17620

Source DB:  PubMed          Journal:  J Med Internet Res        ISSN: 1438-8871            Impact factor:   5.428


  16 in total

1.  Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies.

Authors:  Lushun Jiang; Zhe Wu; Xiaolan Xu; Yaqiong Zhan; Xuehang Jin; Li Wang; Yunqing Qiu
Journal:  J Int Med Res       Date:  2021-03       Impact factor: 1.671

Review 2.  Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review.

Authors:  Aya Sedky Adly; Afnan Sedky Adly; Mahmoud Sedky Adly
Journal:  J Med Internet Res       Date:  2020-08-10       Impact factor: 5.428

3.  Women's attitudes to the use of AI image readers: a case study from a national breast screening programme.

Authors:  Niamh Lennox-Chhugani; Yan Chen; Veronica Pearson; Bernadette Trzcinski; Jonathan James
Journal:  BMJ Health Care Inform       Date:  2021-03

4.  Professional decision making with digitalisation of patient contacts in a medical advice setting: a qualitative study of a pilot project with a chat programme in Sweden.

Authors:  Åsa Cajander; Gustaf Hedström; Sofia Leijon; Marta Larusdottir
Journal:  BMJ Open       Date:  2021-12-02       Impact factor: 2.692

5.  Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey.

Authors:  Thomas Ploug; Anna Sundby; Thomas B Moeslund; Søren Holm
Journal:  J Med Internet Res       Date:  2021-12-13       Impact factor: 5.428

Review 6.  Exploring stakeholder attitudes towards AI in clinical practice.

Authors:  Ian A Scott; Stacy M Carter; Enrico Coiera
Journal:  BMJ Health Care Inform       Date:  2021-12

Review 7.  Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.

Authors:  Kathrin Seibert; Dominik Domhoff; Dominik Bruch; Matthias Schulte-Althoff; Daniel Fürstenau; Felix Biessmann; Karin Wolf-Ostermann
Journal:  J Med Internet Res       Date:  2021-11-29       Impact factor: 5.428

8.  Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study.

Authors:  Stina Matthiesen; Søren Zöga Diederichsen; Mikkel Klitzing Hartmann Hansen; Christina Villumsen; Mats Christian Højbjerg Lassen; Peter Karl Jacobsen; Niels Risum; Bo Gregers Winkel; Berit T Philbert; Jesper Hastrup Svendsen; Tariq Osman Andersen
Journal:  JMIR Hum Factors       Date:  2021-11-26

Review 9.  Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.

Authors:  Chang Seok Bang; Jae Jun Lee; Gwang Ho Baik
Journal:  J Med Internet Res       Date:  2020-09-16       Impact factor: 5.428

Review 10.  Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence-Enabled Clinical Decision Support Systems: Literature Review.

Authors:  Michael Knop; Sebastian Weber; Marius Mueller; Bjoern Niehaves
Journal:  JMIR Hum Factors       Date:  2022-03-24
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