Artificial Intelligence-Based Methods for Precision Medicine: Diabetes
  Risk Prediction
Farida Mohsen Hamada R. H. Al-Absi Noha A. Yousri Nady El Hajj Zubair Shah
The rising prevalence of type 2 diabetes mellitus (T2DM) necessitates the
development of predictive models for T2DM risk assessment. Artificial
intelligence (AI) models are being extensively used for this purpose, but a
comprehensive review of their advancements and challenges is lacking. This
scoping review analyzes existing literature on AI-based models for T2DM risk
prediction. Forty studies were included, mainly published in the past four
years. Traditional machine learning models were more prevalent than deep
learning models. Electronic health records were the most commonly used data
source. Unimodal AI models relying on EHR data were prominent, while only a few
utilized multimodal models. Both unimodal and multimodal models showed
promising performance, with the latter outperforming the former. Internal
validation was common, while external validation was limited. Interpretability
methods were reported in half of the studies. Few studies reported novel
biomarkers, and open-source code availability was limited. This review provides
insights into the current state and limitations of AI-based T2DM risk
prediction models and highlights challenges for their development and clinical
implementation.