Melola Discovery

← Melola Discovery

Journal

Artificial Intelligence and Regional Health Vulnerabilities: A Bibliometric Review of Global Trends in Non-Communicable Disease Research

Unnes Journal of Public Health · Vol. 15 No. 1 (2026) · pp. 93–105

DOI
10.15294/ujph.v15i1.26451
ISSN
2548-7604, 2252-6781
Published
2026-05-09
Publisher
Universitas Negeri Semarang (UNNES) in cooperation with the Association of Indonesian Public Health Experts (Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI))
Language
eng

Abstract

Non-communicable diseases (NCDs), including cardiovascular disease, diabetes, cancer, and chronic respiratory conditions, are the leading causes of death worldwide. These diseases are driven by socioeconomic disparities, unhealthy lifestyles, and environmental factors. AI can improve NCD vulnerability mapping through the use of big data and risk prediction, but faces data and access barriers in developing countries. This study explores how AI can be leveraged to more effectively map regional NCD vulnerability. This study seeks to analyze global research trends in the application of Artificial Intelligence (AI) to map regional vulnerability to non-communicable diseases (NCDs). This study uses a bibliometric analysis of Scopus-indexed, peer-reviewed articles (2006–2025) on AI and NCD vulnerability, processed with Excel and VOSviewer. A total of 3,626 articles were analyzed, showing a sharp rise in AI–NCD research since 2007, peaking in 2024 with 723 publications and 35,148 citations. Frequent keywords included “machine learning”, “COVID-19”, “mental health”, “diabetes”, and “artificial intelligence”. The field is marked by strong international collaboration, led by the US, China, and the UK, with over 96% of studies involving multi-country co-authorship. AI shows strong potential for mapping regional NCD vulnerability, improving early detection, and enhancing predictive risk assessment to strengthen national health policies.

Keywords

Collaboration network of the authors

40 authors · 119 co-authorship links · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • State University of Semarang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Widya Hary CahyatiAffiliation unknown8
Ari YuniastutiState University of Semarang6
Oktia Woro Kasmini HandayaniState University of Semarang5
Yuni WijayantiAffiliation unknown4
Tandiyo RahayuAffiliation unknown4
RR. Sri Ratna RahayuAffiliation unknown3
Arulita Ika FibrianaAffiliation unknown3
Fitri IndrawatiAffiliation unknown3
Irwan BudionoAffiliation unknown3
Muhamad Zakki SaefurrohimAffiliation unknown3
Intan ZainafreeAffiliation unknown3
Muhammad AzinarAffiliation unknown2
Luqman AlwiAffiliation unknown2
Nadiya Arawinda AndarAffiliation unknown2
Sholikun SholikunAffiliation unknown2
Sri Ratna RahayuAffiliation unknown2
Nurul Dwi AstutiAffiliation unknown1
Nur Lissa UtamiAffiliation unknown1
Trinanda Agustina Sapta NingrumAffiliation unknown1
Lukman FauziAffiliation unknown1
Alfiana Ainun NisaAffiliation unknown1
Mochamad Abdul HakamAffiliation unknown1
Fiqie Ulya SidiastahtaAffiliation unknown1
Wiwik Dwi LestariAffiliation unknown1
Safira NuraishaAffiliation unknown1
Silvie Sasria MaharaniAffiliation unknown1
Dani MiarsoAffiliation unknown1
Meilinda Ayu SaputriAffiliation unknown1
Mala RizqiyaAffiliation unknown1
Agtika Yasyfa Nur AzizahAffiliation unknown1
Siti Endah WahyuningsihAffiliation unknown1
Lina ShofiyanahAffiliation unknown1
Dhita Aulia FerdianiAffiliation unknown1
Sugiharto SugihartoAffiliation unknown1
Gisela Habibah AisyahAffiliation unknown1
Sungkowo SungkowoAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/ujph/oai, first harvested 6 Nov 2026, last seen 6 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Artificial Intelligence and Regional Health Vulnerabilities: A Bibliometric Review of Global Trends in Non-Communicable Disease Research</dc:title>
	<dc:creator xml:lang="en">Suhito, Hanif Pandu</dc:creator>
	<dc:creator xml:lang="en">Mahalul Azam</dc:creator>
	<dc:creator xml:lang="en">Dina Nur Anggraini Ningrum</dc:creator>
	<dc:creator xml:lang="en">Iqbal, Usman </dc:creator>
	<dc:subject xml:lang="en">Non-communicable diseases; regional vulnerability; artificial intelligence; bibliometric analysis</dc:subject>
	<dc:description xml:lang="en">Non-communicable diseases (NCDs), including cardiovascular disease, diabetes, cancer, and chronic respiratory conditions, are the leading causes of death worldwide. These diseases are driven by socioeconomic disparities, unhealthy lifestyles, and environmental factors. AI can improve NCD vulnerability mapping through the use of big data and risk prediction, but faces data and access barriers in developing countries. This study explores how AI can be leveraged to more effectively map regional NCD vulnerability. This study seeks to analyze global research trends in the application of Artificial Intelligence (AI) to map regional vulnerability to non-communicable diseases (NCDs). This study uses a bibliometric analysis of Scopus-indexed, peer-reviewed articles (2006–2025) on AI and NCD vulnerability, processed with Excel and VOSviewer. A total of 3,626 articles were analyzed, showing a sharp rise in AI–NCD research since 2007, peaking in 2024 with 723 publications and 35,148 citations. Frequent keywords included “machine learning”, “COVID-19”, “mental health”, “diabetes”, and “artificial intelligence”. The field is marked by strong international collaboration, led by the US, China, and the UK, with over 96% of studies involving multi-country co-authorship. AI shows strong potential for mapping regional NCD vulnerability, improving early detection, and enhancing predictive risk assessment to strengthen national health policies. 
 </dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang (UNNES) in cooperation with the Association of Indonesian Public Health Experts (Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI))</dc:publisher>
	<dc:date>2026-05-09</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/ujph/article/view/26451</dc:identifier>
	<dc:identifier>10.15294/ujph.v15i1.26451</dc:identifier>
	<dc:source xml:lang="en">Unnes Journal of Public Health; Vol. 15 No. 1 (2026); 93-105</dc:source>
	<dc:source>2548-7604</dc:source>
	<dc:source>2252-6781</dc:source>
	<dc:source>10.15294/ujph.v15i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/ujph/article/view/26451/9605</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Unnes Journal of Public Health</dc:rights>
</oai_dc:dc>