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

Anxiety and depression were known to bring detrimental outcome in patients with ischemic heart disease (IHD). Notwithstanding their high prevalence and catastrophic impact, anxiety and depression were unrecognized and untreated. The aim of this study was to determine the prevalence of anxiety and depression among IHD patients and the association of this condition with clinical and selected demographic factors. This was a cross-sectional study on 100 IHD patients admitted to medical ward in UKMMC. Patients diagnosed to have IHD were randomly assessed using Hospital Anxiety and Depression Scale (HADS) and Perceived Social Support (PSS) Questionnaire. Socio-demographic data were obtained by direct interview. Fifteen percent of IHD patients in this sample were noted to have anxiety, fourteen percent noted to have depression while thirty two percent was noted to have both anxiety and depression. Patients’ age group and the duration of illness were found to have significant association with anxiety. Socio-demographic data were obtained by direct interview. Fifteen percent of IHD patients in this sample were noted to have anxiety, fourteen percent noted to have depression while thirty two percent was noted to have both anxiety and depression. Patients’ age group and the duration of illness were found to have significant association wit¬h anxiety. The other clinical and selected demographic factors such as gender, race, marital status, education level, occupation, co-existing medical illness and social support were not found to be significantly associated with anxiety or depression among the IHD patients. In conclusion, proper assessment of anxiety and depression in IHD patients, with special attention to patients’ age and duration of illness should be carried out routinely to help avert detrimental consequences.​