Enhancing Care in Type 1 Diabetes with Artificial Intelligence Driven Clinical Decision Support Systems

Area of Science:

• Artificial Intelligence in Healthcare
• Digital Health Technologies
• Chronic Disease Management

Background:

• Type 1 diabetes (T1D) necessitates extensive daily self-management and ongoing clinical care.
• Artificial intelligence (AI) is increasingly applicable to diabetes care tasks.
• AI-driven clinical decision support systems (AI-CDSS) integrate diverse data sources for enhanced T1D management.

Purpose of the Study:

• To explore the role of AI-CDSS in personalizing and improving T1D self-management.
• To examine how AI-CDSS can transform risk prediction, detection, and management of T1D for healthcare professionals (HCPs).
• To assess the potential of AI in streamlining healthcare services and resource allocation for T1D care.

Main Methods:

• Integration of data from wearables (smartwatches, activity trackers), continuous glucose monitors (CGM), insulin pumps, and smartpens.
• Application of AI algorithms for data analysis and pattern recognition in T1D.
• Development of AI-CDSS to provide personalized, predictive, and proactive management recommendations.

Main Results:

• AI-CDSS can support individuals with T1D in achieving more personalized, predictive, and proactive daily self-management.
• AI-CDSS are altering approaches to risk prediction, detection, and assessment of presymptomatic T1D for HCPs.
• AI-CDSS can assist HCPs in prioritizing clinical management and optimizing resource allocation.

Conclusions:

• AI technologies promise significant support for T1D daily life and healthcare services.
• Widespread adoption requires clinical validation, regulatory approval, and comprehensive training on AI-CDSS.
• Realizing AI's potential in T1D necessitates addressing challenges in trust, adoption, equity, efficacy, and regulatory compliance.