Artificial Intelligence-Based Methods for Precision Medicine: Diabetes Risk Prediction

TL;DR

This review examines recent AI models for predicting type 2 diabetes risk, highlighting their methodologies, performance, and challenges in clinical application, with a focus on data sources, validation, and interpretability.

Contribution

It provides a comprehensive overview of AI-based T2DM risk prediction models, emphasizing current trends, limitations, and future challenges for clinical integration.

Findings

Deep learning models are less common than traditional machine learning.

Multimodal models tend to outperform unimodal models.

External validation of models is limited.

Abstract

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.