Use of technology and AI tools in type 2 diabetes

The growing burden and heterogeneity of type 2 diabetes mellitus (T2DM) require management strategies that extend beyond episodic clinic visits. Digital health technologies mHealth platforms, wearables, telemedicine, continuous glucose monitoring (CGM), and connected insulin delivery now generate high-frequency, real-world data that can support continuous care. Evidence suggests that the use of mHealth platforms among individuals with T2DM is associated with an approximate 0.5% reduction in HbA1c, highlighting their potential clinical benefit(1). When coupled with artificial intelligence (AI), these systems shift diabetes management toward prediction, personalization, and proactive intervention. This review appraises the clinical evidence for technology-enabled T2DM care and examines how AI improves risk stratification, detection of dysglycemia patterns, treatment optimization, and behavioural support. We discuss implementation challenges related to data quality, interoperability, governance, explainability, bias, regulatory oversight, and equity, with attention to low- and middle-income contexts. We propose a roadmap for translating AI-enabled diabetes ecosystems into routine care while maximizing effectiveness, safety, and accessibility.