Artificial Intelligence in Type II Diabetes Mellitus: Screening, Treatment, and Complication

Authors

  • Airine Stefanie Lians Universitas Katolik Indonesia Atma Jaya Author
  • Andi Miyanza Rezkyawan Lakipadada Tunru Universitas Katolik Indonesia Atma Jaya Author
  • Chindia Chindia Universitas Katolik Indonesia Atma Jaya Author
  • Juan Alexandra Prasetyo Universitas Katolik Indonesia Atma Jaya Author
  • Justin Kie Universitas Katolik Indonesia Atma Jaya Author
  • Raffael Christian Universitas Katolik Indonesia Atma Jaya Author
  • Sherlyn Sean Universitas Katolik Indonesia Atma Jaya Author
  • Victoria Larasati Universitas Katolik Indonesia Atma Jaya Author
  • Liauw Djai Yen Universitas Kristen Krida Wacana Author

DOI:

https://doi.org/10.15294/kemas.v20i4.1138

Keywords:

Artificial intelligence, Type II diabetes mellitus, Complication, Screening, Treatment

Abstract

Type II diabetes mellitus is one of the chronic metabolic diseases that are associated with insulin resistance. Type II diabetes mellitus incidence continues to increase each year and may cause various health complications, even death. Addressing early detection and appropriate treatment is important in decreasing the incidence of type II diabetes mellitus and improving the quality of life in diabetic patients. The potential of artificial intelligence in healthcare is expected to assist in screening, therapy management, and even detection of type II diabetes mellitus complications. Despite limited literature, this study aims to understand the benefit of AI in assisting health workers in screening and managing type II diabetes mellitus. Searches are conducted with search engines, such as PubMed, Science Direct, and Google Scholar, with the keywords “Artificial Intelligence” and “Diabetes Mellitus Type 2”, as well as their synonyms. The search results in twenty English and Indonesian studies were published in the last ten years. These various studies found that many Artificial intelligence models developed to assist in screening, therapy management, and detect complications in patients with type II diabetes mellitus.

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Published

2025-04-30

Article ID

1138

Issue

Section

Articles

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