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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) · hlm. 93–105

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

Abstrak

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.

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  • State University of Semarang
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Widya Hary CahyatiAfiliasi tidak diketahui8
Ari YuniastutiState University of Semarang6
Oktia Woro Kasmini HandayaniState University of Semarang5
Yuni WijayantiAfiliasi tidak diketahui4
Tandiyo RahayuAfiliasi tidak diketahui4
RR. Sri Ratna RahayuAfiliasi tidak diketahui3
Arulita Ika FibrianaAfiliasi tidak diketahui3
Fitri IndrawatiAfiliasi tidak diketahui3
Irwan BudionoAfiliasi tidak diketahui3
Muhamad Zakki SaefurrohimAfiliasi tidak diketahui3
Intan ZainafreeAfiliasi tidak diketahui3
Muhammad AzinarAfiliasi tidak diketahui2
Luqman AlwiAfiliasi tidak diketahui2
Nadiya Arawinda AndarAfiliasi tidak diketahui2
Sholikun SholikunAfiliasi tidak diketahui2
Sri Ratna RahayuAfiliasi tidak diketahui2
Nurul Dwi AstutiAfiliasi tidak diketahui1
Nur Lissa UtamiAfiliasi tidak diketahui1
Trinanda Agustina Sapta NingrumAfiliasi tidak diketahui1
Lukman FauziAfiliasi tidak diketahui1
Alfiana Ainun NisaAfiliasi tidak diketahui1
Mochamad Abdul HakamAfiliasi tidak diketahui1
Fiqie Ulya SidiastahtaAfiliasi tidak diketahui1
Wiwik Dwi LestariAfiliasi tidak diketahui1
Safira NuraishaAfiliasi tidak diketahui1
Silvie Sasria MaharaniAfiliasi tidak diketahui1
Dani MiarsoAfiliasi tidak diketahui1
Meilinda Ayu SaputriAfiliasi tidak diketahui1
Mala RizqiyaAfiliasi tidak diketahui1
Agtika Yasyfa Nur AzizahAfiliasi tidak diketahui1
Siti Endah WahyuningsihAfiliasi tidak diketahui1
Lina ShofiyanahAfiliasi tidak diketahui1
Dhita Aulia FerdianiAfiliasi tidak diketahui1
Sugiharto SugihartoAfiliasi tidak diketahui1
Gisela Habibah AisyahAfiliasi tidak diketahui1
Sungkowo SungkowoAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/ujph/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<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>