Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review
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 signifi...
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| Format: | Article |
| Language: | English |
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Interna Publishing
2024-10-01
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| Series: | Acta Medica Indonesiana |
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| Online Access: | https://actamedindones.org/index.php/ijim/article/view/2730 |
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| author | Aqsha Nur Defin Yumnanisha Sydney Tjandra Adang Bachtiar Dante Saksono Harbuwono |
| author_facet | Aqsha Nur Defin Yumnanisha Sydney Tjandra Adang Bachtiar Dante Saksono Harbuwono |
| author_sort | Aqsha Nur |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2d0d70371c9b47cb9e5fb1336aff9f01 |
| institution | Kabale University |
| issn | 0125-9326 2338-2732 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Interna Publishing |
| record_format | Article |
| series | Acta Medica Indonesiana |
| spelling | doaj-art-2d0d70371c9b47cb9e5fb1336aff9f012025-01-27T04:12:06ZengInterna PublishingActa Medica Indonesiana0125-93262338-27322024-10-01564706Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature ReviewAqsha Nur0Defin Yumnanisha1Sydney Tjandra2Adang Bachtiar3Dante Saksono Harbuwono4Faculty of Public Health, Universitas Indonesia, Depok, IndonesiaFaculty of Medicine Universitas Indonesia, Jakarta, IndonesiaFaculty of Medicine Universitas Indonesia, Jakarta, IndonesiaFaculty of Public Health, Universitas Indonesia, Depok, IndonesiaDivision of Endocrinology and Metabolism, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia – Cipto Mangunkusumo Hospital, Jakarta, IndonesiaThe 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.https://actamedindones.org/index.php/ijim/article/view/2730diabetes mellitusartificial intelligencescreeningdiagnosis |
| spellingShingle | Aqsha Nur Defin Yumnanisha Sydney Tjandra Adang Bachtiar Dante Saksono Harbuwono Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review Acta Medica Indonesiana diabetes mellitus artificial intelligence screening diagnosis |
| title | Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review |
| title_full | Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review |
| title_fullStr | Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review |
| title_full_unstemmed | Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review |
| title_short | Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review |
| title_sort | potential use and limitation of artificial intelligence to screen diabetes mellitus in clinical practice a literature review |
| topic | diabetes mellitus artificial intelligence screening diagnosis |
| url | https://actamedindones.org/index.php/ijim/article/view/2730 |
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