Introduction: Diabetes affects 15.6% of Malaysian adults and remains a leading cause of complications and mortality. Although locally developed artificial intelligence (AI) tools have shown strong technical performance, their integration with clinicians’ decision-making in primary care remains fragmented. This perspective proposes a synergistic AI– clinician framework to deliver truly personalized proactive diabetes care. Methodology: A multidisciplinary team designed a clinical workflow that integrates three validated Malaysian AI components: (1) LightGBM (LGBM) model trained on Malaysian National Diabetes Registry (MNDR) data for complication risk prediction, (2) DR.MATA AI retinopathy screening with automated triage, and (3) AI-enhanced mobile apps for real-time self-management. These layers feed into a single secure clinician dashboard. The framework was modelled using existing datasets (MNDR 2011–2021, NHMS 2023, DR.MATA validation cohort >14,000 images) and aligned with the National AI Roadmap 2021–2025 and the forthcoming National AI Action Plan 2026–2030. Results: The LightGBM model achieved ROC-AUC values of 0.84 (mortality), 0.71 (retinopathy/nephropathy), 0.66 (ischemic heart disease), and 0.74 (stroke). DR.MATA demonstrated 93.3% accuracy with automated triage. When integrated, modelling suggests the framework could reduce major complications by approximately 20–30% and unnecessary referrals by up to 35%. The system runs automatically upon opening the patient record, requiring no extra clinician steps. Conclusion Synergizing AI with clinicians’ expertise offers a scalable solution for Malaysian primary health clinics. A 12-month national pilot is recommended to generate real-world evidence and inform the National AI Action Plan 2026–2030.
Artificial Intelligence ; Diabetes Mellitus