JMIR Publications

ABSTRACT

Type 2 diabetes mellitus (T2D) is a rapidly growing global health concern requiring innovative treatment methods. Ozempic® (Semaglutide), a Glucagon-Like Peptide-1 (GLP-1) receptor agonist, has proven consistent effectiveness in lowering blood glucose level, supporting weight loss, and minimizing cardiovascular complications. In parallel, artificial intelligence (AI) elevates diabetes care yet complements these efforts by converting raw data from wearable devices, Electronic Health Records (EHRs), and medical imaging into practical insights for efficient tailored and customized treatment plans. This systematic review examines current evidence of AI-driven methods to optimize Ozempic®-based T2D therapy. Eighteen peer-reviewed articles were identified revealing four Dominant thematic clusters: (1) patient stratification and risk prediction, (2) AI-enhanced imaging for body composition changes, (3) cardiovascular and metabolic risk assessment, and (4) personalized AI-driven dosage. Across multiple metrics-Glycated Hemoglobin (HbA1c) reduction, weight loss, cardiovascular bene- fits and adverse event mitigation- AI-based approaches outperformed standard fixed-dose regimens. A theoretical framework is proposed for AI-Ozempic® integration, with continuous data collection, AI processing, clinical decision support, real-time support and real-time feedback and modeling iteration refinement cycles. However, significant gaps remain a persistent challenge, including the need for large-scale randomized controlled trials (RCTs), longer follow-up periods, explainable AI (XAI) models, regulatory validation, and practical strategies for routine clinical implementation. The findings emphasize the AI’s potential to transform semaglutide therapy while delineating important paths for future research.