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Cited by 125Artificial Intelligence in HealthcareMachine Learning in HealthcareArtificial Intelligence in Healthcare and Education

A scoping review of artificial intelligence-based methods for diabetes risk prediction

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Mohsen, Farida, Al-Absi, Hamada R. H., Yousri, Noha A., El Hajj, Nady, Shah, Zubair·2023·npj Digital Medicine
DOI 10.1038/s41746-023-00933-5PMC PMC10600138
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Abstract

The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.

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  • AI is now being utilized to predict an individual's risk for developing type 2 diabetes (T2DM) and potential complications.

    “Beyond intervention and treatment, AI is now being utilized to predict an individual’s risk for developing type 2 diabetes (T2DM) and potential complications.”

    From AI-based diabetes care: risk prediction models and implementation concerns (2024) · cites this paper as [5]

    The Introduction and Results describe AI models used to predict individuals’ risk of developing T2DM. The Introduction also says precision prognostics aims to predict T2DM and its complications, but the review does not establish that the AI models it examines predict complications. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Multimodal AI approaches were superior to unimodal models for diabetes risk prediction in the Mohsen et al. scoping review.

    “A scoping review of 40 studies by Mohsen et al. shows that while most studies used unimodal AI models, multimodal approaches were superior because they integrate multiple types of data.”

    From AI-based diabetes care: risk prediction models and implementation concerns (2024) · cites this paper as [5]

  • A modest number (n = 10) of studies in the Mohsen et al. review adopted multimodal approaches combining EHR with multi-omics or medical imaging data.

    “Although a modest number (n = 10) studies adopted multimodal approaches (combining EHR with multi-omics or medical imaging data), these were shown to be superior to unimodal models (n = 30).”

    From AI-based diabetes care: risk prediction models and implementation concerns (2024) · cites this paper as [5]

  • Most models in the Mohsen et al. review were classical machine learning models with electronic health records as the predominant data source.

    “Of these models, most were classical machine learning models with electronic health records as the predominant data source.”

    From AI-based diabetes care: risk prediction models and implementation concerns (2024) · cites this paper as [5]

  • Only five studies in the Mohsen et al. review provided insights into calibration of models.

    “Only five studies provided insights into calibration of models and only five conducted external validations.”

    From AI-based diabetes care: risk prediction models and implementation concerns (2024) · cites this paper as [5]

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Cited by

Sorted by citations

Authors
Mohsen, F., Al-Absi, H. R. H., Yousri, N. A., El Hajj, N., & Shah, Z.
Affiliations
Hamad bin Khalifa University (QA)
Corresponding author
Shah, Zubair
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2023-10-25
Volume
6
Issue
1
Pages
197-197
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-023-00933-5
PMC
PMC10600138
OpenAlex
W4387949693
Cited by
125
References
14
Citations by year
2026: 37 · 2025: 53 · 2024: 25 · 2023: 10
FWCI
4.90
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Digital Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • Greater South Information System
Funders
  • Fonds National de la Recherche Luxembourg
  • Qatar National Research Fund
  • Qatar National Library
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
type 2 diabetes, diabetes risk prediction, artificial intelligence, machine learning, electronic health records, multi-omics, multimodal models, external validation, model calibration, interpretability, risk predictors, clinical integration

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