AI and Digital Diabetes Care: Awareness, Utilisation and Perspectives from Malaysia

Chee Keong SeeMalaysia Speaker AI and Digital Diabetes Care: Awareness, Utilisation and Perspectives from MalaysiaMalaysia’s substantial burden of Type 2 Diabetes (T2D) necessitates a transition from a conventional “standard-of-care” approach toward a more individualized “precision-of-care” model. In this context, Artificial Intelligence (AI)–driven Clinical Decision Support Systems (CDSS) are emerging as promising tools to address persistent clinical and treatment inertia. AI in Insulin Titration Recent evidence evaluating a real-time AI-assisted insulin titration system for glycaemic control in patients with T2D demonstrates considerable clinical potential. By integrating Continuous Glucose Monitoring (CGM) data with historical insulin dosing patterns, the system generates individualized titration recommendations. Clinical findings indicate non-inferiority to senior endocrinologists in insulin dose adjustment. Notably, use of the system was associated with improvements in Time in Range (TIR), enhanced day-to-day glycaemic stability, and a significant reduction in nocturnal hypoglycaemia. Specialist Perspective Malaysian endocrinologists have expressed cautious optimism regarding the integration of AI into diabetes management. Rather than functioning as a replacement for clinician expertise or doctor–patient interaction, AI-based systems are regarded as adjunctive decision-support tools. These systems may alleviate cognitive and administrative burden, thereby allowing clinicians to allocate greater attention to complex case management, comorbidity optimization, and individualized patient counselling. Adoption Drivers and Barriers The successful implementation of AI-driven CDSS is influenced by both technical and socio-behavioral factors. Adoption is strongly associated with performance expectancy, particularly perceived improvements in efficiency and clinical productivity, as well as positive peer influence within the medical community. Conversely, concerns surrounding data privacy, workflow disruption, system interoperability, and digital literacy—especially among older patient populations—remain significant barriers to widespread integration. Future Directions To translate technological innovation into sustainable clinical impact, strategic priorities should include seamless integration with existing Electronic Medical Record (EMR) systems, robust data governance and cybersecurity frameworks, and structured AI competency training for healthcare professionals. Ultimately, AI should be conceptualized not as a substitute for clinical judgment, but as a complementary enabler of scalable, precise, and patient-centered diabetes care in Malaysia.