# Application of AI and digital health tools in public health management of T2DM: from mechanism prediction to personalized treatment

## TL;DR

AI and digital health tools are transforming T2DM management by enabling precise prediction, personalized treatment, and efficient public health strategies.

## Contribution

This review highlights how AI integrates multi-omics and environmental data to improve T2DM prediction and management, especially in low-resource settings.

## Key findings

- AI reveals key mechanisms like gene–environment interactions and β-cell dysfunction, enhancing early T2DM screening.
- Digital health tools like CGM and mHealth apps improve remote monitoring and personalized treatment adherence.
- AI optimizes public health resource allocation and disease burden assessment for chronic disease control.

## Abstract

Type 2 diabetes mellitus (T2DM) poses a significant global public health challenge, with its prevalence escalating continuously and disproportionately affecting low- and middle-income countries (LMICs), imposing a substantial burden on healthcare systems. Traditional management models have limitations in disease prediction, personalized treatment, and public health intervention. Artificial intelligence (AI) and digital health technologies provide novel insights for precise prediction and intelligent management of T2DM. This review systematically summarizes research progress in AI’s role in deciphering T2DM pathogenesis, personalized treatment, and public health management. By integrating multi-omics and environmental data, AI reveals key mechanisms including gene–environment (G × E) interactions, β-cell dysfunction, and inflammatory pathways, significantly enhancing early screening and risk prediction. In clinical management, AI combined with digital health tools [e.g., continuous glucose monitoring (CGM), wearable devices, and mobile health (mHealth) apps] facilitates remote monitoring, medication optimization, and personalized interventions, improving treatment adherence and health management efficiency. At the public health level, AI optimizes resource allocation and disease burden assessment, promoting chronic disease prevention and control model transformation. Future efforts should prioritize developing low-resource-adapted tools, strengthening data privacy protection tailored to LMICs, and addressing algorithmic fairness and the digital divide to ensure safe, equitable, and sustainable AI application in global T2DM management. Overall, AI and digital health integration is driving T2DM management towards an intelligent and precision-based era, with the potential to reduce disparities in LMICs.