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

Infection of cutaneous larva migrans is usually acquired in the tropical region and mainly affects children. It is generally diagnosed with symptoms of severe itchiness and skin lesion that appears as a migrating erythematous linear lesion. It often occurs with a history of skin exposure to soil. Atypical presentation of the infection causes difficulty in diagnosis and delays treatment for the patient. Once diagnosed, cutaneous larva migrans is treated easily with helminth medications such as albendazole. Recognition of the skin lesion of cutaneous larva migrans in the atypical presentation of the infection for prompt treatment will comfort and prevent secondary infection complications. This case describes a presentation of bullous eruption in a child infected with cutaneous larva migrans.