Artificial intelligence in healthcare business ecosystem: a bibliometric study

The use of artificial intelligence (AI) in healthcare is rapidly increasing. Digital health start-ups are bringing new digital technologies and services to the market, allowing for cost savings and service improvements in the healthcare sector. However, successful integration of AI into the healthca...

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Main Authors: Ismail, Albert Feisal @ Muhd Feisal, Mohd Sam, Mohd Fazli, Abu Bakar, Kamarudin, Ahamat, Amiruddin, Adam, Sabrinah, Qureshi, Muhammad Imran
Format: Article
Language:English
Published: International Association of Online Engineering 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26670/2/2-3-1-1-PENULIS%20UTAMA%20BERINDEX%20SCOPUS.PDF
http://eprints.utem.edu.my/id/eprint/26670/
https://online-journals.org/index.php/i-joe/article/view/32251/11663
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spelling my.utem.eprints.266702023-03-24T11:19:04Z http://eprints.utem.edu.my/id/eprint/26670/ Artificial intelligence in healthcare business ecosystem: a bibliometric study Ismail, Albert Feisal @ Muhd Feisal Mohd Sam, Mohd Fazli Abu Bakar, Kamarudin Ahamat, Amiruddin Adam, Sabrinah Qureshi, Muhammad Imran The use of artificial intelligence (AI) in healthcare is rapidly increasing. Digital health start-ups are bringing new digital technologies and services to the market, allowing for cost savings and service improvements in the healthcare sector. However, successful integration of AI into the healthcare ecosystem is required to realise its full potential. A digital ecosystem approach can be used to achieve this integration. Using bibliometric analysis, this research seeks to provide a clear overview of artificial intelligence in the digital healthcare ecosystem by analysing the published literature in the field. A systematic literature search was conducted on an article extracted from the Scopus database related to artificial intelligence in the digital healthcare ecosystem. A search technique was devised in order to collect relevant publications and bibliographic data (e.g., country, research area, sources, and author). The VOS viewer was used to visualise the co-authorship networks of countries as well as the co-occurrence of author keywords (Leiden University). This study is unique in a way that it presents a comprehensive picture of global efforts of the use of artificial intelligence in the healthcare business ecosystem. Academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these areas in the future will benefit from the insights and research directions of this study. International Association of Online Engineering 2022-07-11 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26670/2/2-3-1-1-PENULIS%20UTAMA%20BERINDEX%20SCOPUS.PDF Ismail, Albert Feisal @ Muhd Feisal and Mohd Sam, Mohd Fazli and Abu Bakar, Kamarudin and Ahamat, Amiruddin and Adam, Sabrinah and Qureshi, Muhammad Imran (2022) Artificial intelligence in healthcare business ecosystem: a bibliometric study. International Journal Of Online And Biomedical Engineering, 18 (9). pp. 100-114. ISSN 2626-8493 https://online-journals.org/index.php/i-joe/article/view/32251/11663 10.3991/ijoe.v18i09.32251
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description The use of artificial intelligence (AI) in healthcare is rapidly increasing. Digital health start-ups are bringing new digital technologies and services to the market, allowing for cost savings and service improvements in the healthcare sector. However, successful integration of AI into the healthcare ecosystem is required to realise its full potential. A digital ecosystem approach can be used to achieve this integration. Using bibliometric analysis, this research seeks to provide a clear overview of artificial intelligence in the digital healthcare ecosystem by analysing the published literature in the field. A systematic literature search was conducted on an article extracted from the Scopus database related to artificial intelligence in the digital healthcare ecosystem. A search technique was devised in order to collect relevant publications and bibliographic data (e.g., country, research area, sources, and author). The VOS viewer was used to visualise the co-authorship networks of countries as well as the co-occurrence of author keywords (Leiden University). This study is unique in a way that it presents a comprehensive picture of global efforts of the use of artificial intelligence in the healthcare business ecosystem. Academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these areas in the future will benefit from the insights and research directions of this study.
format Article
author Ismail, Albert Feisal @ Muhd Feisal
Mohd Sam, Mohd Fazli
Abu Bakar, Kamarudin
Ahamat, Amiruddin
Adam, Sabrinah
Qureshi, Muhammad Imran
spellingShingle Ismail, Albert Feisal @ Muhd Feisal
Mohd Sam, Mohd Fazli
Abu Bakar, Kamarudin
Ahamat, Amiruddin
Adam, Sabrinah
Qureshi, Muhammad Imran
Artificial intelligence in healthcare business ecosystem: a bibliometric study
author_facet Ismail, Albert Feisal @ Muhd Feisal
Mohd Sam, Mohd Fazli
Abu Bakar, Kamarudin
Ahamat, Amiruddin
Adam, Sabrinah
Qureshi, Muhammad Imran
author_sort Ismail, Albert Feisal @ Muhd Feisal
title Artificial intelligence in healthcare business ecosystem: a bibliometric study
title_short Artificial intelligence in healthcare business ecosystem: a bibliometric study
title_full Artificial intelligence in healthcare business ecosystem: a bibliometric study
title_fullStr Artificial intelligence in healthcare business ecosystem: a bibliometric study
title_full_unstemmed Artificial intelligence in healthcare business ecosystem: a bibliometric study
title_sort artificial intelligence in healthcare business ecosystem: a bibliometric study
publisher International Association of Online Engineering
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26670/2/2-3-1-1-PENULIS%20UTAMA%20BERINDEX%20SCOPUS.PDF
http://eprints.utem.edu.my/id/eprint/26670/
https://online-journals.org/index.php/i-joe/article/view/32251/11663
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score 13.160551