Use of technology and AI tools in type 2 diabetes

Area of Science:

• Digital Health
• Artificial Intelligence
• Diabetes Mellitus Management

Background:

• Type 2 diabetes mellitus (T2DM) presents a growing global health challenge requiring innovative management strategies.
• Traditional episodic clinic visits are insufficient for the complex and heterogeneous nature of T2DM.
• Digital health technologies generate high-frequency real-world data crucial for continuous patient care.

Purpose of the Study:

• To review the clinical evidence supporting technology-enabled T2DM care.
• To examine the role of artificial intelligence (AI) in enhancing T2DM management.
• To identify challenges and propose a roadmap for integrating AI-driven diabetes ecosystems into routine clinical practice.

Main Methods:

• Comprehensive review of clinical evidence for digital health tools in T2DM.
• Analysis of AI applications in risk stratification, pattern detection, treatment optimization, and behavioral support.
• Discussion of implementation challenges including data quality, interoperability, governance, bias, and equity.

Main Results:

• mHealth platforms show potential clinical benefits, with evidence suggesting an approximate 0.5% HbA1c reduction in T2DM patients.
• AI integration shifts diabetes management towards prediction, personalization, and proactive interventions.
• Significant implementation challenges exist, particularly concerning data, regulation, and equitable access.

Conclusions:

• AI-enabled digital health ecosystems hold promise for revolutionizing T2DM care by enabling continuous, personalized, and proactive management.
• Addressing challenges in data, governance, and equity is essential for widespread adoption.
• A strategic roadmap is proposed to maximize the effectiveness, safety, and accessibility of these advanced diabetes management systems.