Revolutionising healthcare with artificial intelligence : A bibliometric analysis of 40 years of progress in health systems

Journal article

Hussain, Walayat, Mabrok, Mohamed, Gao, Honghao, Rabhi, Fethi A. and Rashed, Essam A.. (2024). Revolutionising healthcare with artificial intelligence : A bibliometric analysis of 40 years of progress in health systems. Digital Health. 10, pp. 1-20. https://doi.org/10.1177/20552076241258757
AuthorsHussain, Walayat, Mabrok, Mohamed, Gao, Honghao, Rabhi, Fethi A. and Rashed, Essam A.
Abstract

The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include ‘ML’, ‘Deep Learning’, and ‘Artificial Intelligence’.

KeywordsMachine learning; artificial intelligence in health; health prediction; medical systems; bibliometric; citation analysis; web of Science; AI in health
Year01 Jan 2024
JournalDigital Health
Journal citation10, pp. 1-20
PublisherSage Publications Ltd. (UK)
ISSN2055-2076
Digital Object Identifier (DOI)https://doi.org/10.1177/20552076241258757
Web address (URL)https://journals.sagepub.com/doi/10.1177/20552076241258757
Open accessPublished as ‘gold’ (paid) open access
Research or scholarlyResearch
Page range1-20
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Open
Output statusPublished
Publication dates
Print28 May 2024
Publication process dates
Accepted14 May 2024
Deposited17 Jul 2024
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© The Author(s) 2024.

This article is distributed under the terms of the Creative Commons Attribution-NoDerivs 4.0 License ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ) which permits any use, reproduction and distribution of the work as published without adaptation or alteration, provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
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