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Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review

ACTA MEDICA INDONESIANA · Vol. 56 No. 4 (2025)

ISSN
2338-2732, 0125-9326
Terbit
2025-01-21
Penerbit
The Indonesian Society of Internal Medicine
Bahasa
eng

Abstrak

The burden of undiagnosed diabetes mellitus (DM) is substantial, with approximately 240 million individuals globally unaware of their condition, disproportionately affecting low- and middle-income countries (LMICs), including Indonesia. Without screening, DM and its complications will impose significant pressure on healthcare systems. Current clinical practices for screening and diagnosing DM primarily involve blood or laboratory-based testing which possess limitations on access and cost. To address these challenges, researchers have developed risk-scoring tools to identify high-risk populations. However, considering generalizability, artificial intelligence (AI) technologies offer a promising approach, leveraging diverse data sources for improved accuracy. AI models (i.e., machine learning and deep learning) have yielded prediction performances of up to 98% in various diseases. This article underscores the potential of AI-driven approaches in reducing the burden of DM through accurate prediction of undiagnosed diabetes while highlighting the need for continued innovation and collaboration in healthcare delivery.

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  • University of Indonesia
  • Universitas Teknologi Sumbawa
  • Rumah Sakit Umum Pusat Nasional Dr. Cipto Mangunkusumo
  • Sekolah Tinggi Ilmu Kesehatan Husada Jombang
  • Afiliasi tidak diketahui
  • Organisasi berbeda
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Imam SubektiAfiliasi tidak diketahui3
Dicky Levenus TahaparyAfiliasi tidak diketahui3
Farid KurniawanAfiliasi tidak diketahui3
Tri Juli Edi TariganUniversity of Indonesia2
Dyah PurnamasariAfiliasi tidak diketahui2
Pradana SoewondoAfiliasi tidak diketahui2
Suharko SoebardiAfiliasi tidak diketahui2
Wismandari WisnuAfiliasi tidak diketahui2
Budiman DarmowidjojoAfiliasi tidak diketahui2
Indrati SuroyoAfiliasi tidak diketahui2
Alvita Dewi SiswoyoAfiliasi tidak diketahui2
Soehartati GondhowiardjoAfiliasi tidak diketahui2
Henry KodratAfiliasi tidak diketahui2
Esthika DewiastyAfiliasi tidak diketahui1
Laurentius Aswin PramonoAfiliasi tidak diketahui1
Em YunirUniversity of Indonesia1
Lisnawati LisnawatiUniversitas Teknologi Sumbawa1
Erwin Danil YulianAfiliasi tidak diketahui1
Agnes StephanieAfiliasi tidak diketahui1
Benyamin MakesAfiliasi tidak diketahui1
Benny ZulkarnaienAfiliasi tidak diketahui1
Bambang HermaniAfiliasi tidak diketahui1
Marlinda AdhamAfiliasi tidak diketahui1
M SidikAfiliasi tidak diketahui1
Syntia NusantiRumah Sakit Umum Pusat Nasional Dr. Cipto Mangunkusumo1
Salmarezka DewiputriAfiliasi tidak diketahui1
Hernawita SuharkoAfiliasi tidak diketahui1
Gusti G SuardanaAfiliasi tidak diketahui1
Vally WulaniAfiliasi tidak diketahui1
Kuntjoro HarimurtiAfiliasi tidak diketahui1
Syahidatul WafaAfiliasi tidak diketahui1
Evy YunihastutiAfiliasi tidak diketahui1
Cleopas Martin RumendeAfiliasi tidak diketahui1
Ketut SuastikaAfiliasi tidak diketahui1
Heri WibowoSekolah Tinggi Ilmu Kesehatan Husada Jombang1

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) http://www.actamedindones.org/index.php/ijim/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review</dc:title>
	<dc:creator>Nur, Aqsha</dc:creator>
	<dc:creator>Yumnanisha, Defin</dc:creator>
	<dc:creator>Tjandra, Sydney</dc:creator>
	<dc:creator>Bachtiar, Adang</dc:creator>
	<dc:creator>Harbuwono, Dante Saksono</dc:creator>
	<dc:subject xml:lang="en-US">diabetes mellitus</dc:subject>
	<dc:subject xml:lang="en-US">artificial intelligence</dc:subject>
	<dc:subject xml:lang="en-US">screening</dc:subject>
	<dc:subject xml:lang="en-US">diagnosis</dc:subject>
	<dc:description xml:lang="en-US">The burden of undiagnosed diabetes mellitus (DM) is substantial, with approximately 240 million individuals globally unaware of their condition, disproportionately affecting low- and middle-income countries (LMICs), including Indonesia. Without screening, DM and its complications will impose significant pressure on healthcare systems. Current clinical practices for screening and diagnosing DM primarily involve blood or laboratory-based testing which possess limitations on access and cost. To address these challenges, researchers have developed risk-scoring tools to identify high-risk populations. However, considering generalizability, artificial intelligence (AI) technologies offer a promising approach, leveraging diverse data sources for improved accuracy. AI models (i.e., machine learning and deep learning) have yielded prediction performances of up to 98% in various diseases. This article underscores the potential of AI-driven approaches in reducing the burden of DM through accurate prediction of undiagnosed diabetes while highlighting the need for continued innovation and collaboration in healthcare delivery.</dc:description>
	<dc:publisher xml:lang="en-US">The Indonesian Society of Internal Medicine</dc:publisher>
	<dc:date>2025-01-21</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-US">Literature review</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>http://www.actamedindones.org/index.php/ijim/article/view/2730</dc:identifier>
	<dc:source xml:lang="en-US">Acta Medica Indonesiana; Vol. 56 No. 4 (2024): Acta Medica Indonesiana; 563</dc:source>
	<dc:source>2338-2732</dc:source>
	<dc:source>0125-9326</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>http://www.actamedindones.org/index.php/ijim/article/view/2730/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Aqsha Nur</dc:rights>
</oai_dc:dc>