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AI-Enabled Personalization of Semaglutide Therapy in Type 2 Diabetes: Systematic Review With an Integration Framework
Ghinwa Barakat1, Samer El Hajj Hassan2,3,4, Hanane Akhdar1,5
1Biological and Chemical Sciences Department, School of Arts And Sciences, Lebanese International University, Beirut, Lebanon.
JMIR AI
|March 9, 2026
Summary
Artificial intelligence (AI) enhances Ozempic (semaglutide) therapy for type 2 diabetes by personalizing treatment plans. AI integration shows improved outcomes in blood glucose control, weight loss, and cardiovascular health compared to standard methods.
Area of Science:
- Endocrinology and Metabolism
- Medical Artificial Intelligence
- Pharmacotherapy
Background:
- Type 2 diabetes mellitus (T2D) presents a growing global challenge, necessitating advanced therapeutic strategies.
- Ozempic (semaglutide), a GLP-1 receptor agonist, demonstrates efficacy in glycemic control, weight management, and cardiovascular risk reduction.
- Artificial intelligence (AI) offers transformative potential in diabetes care by extracting actionable insights from diverse health data sources.
Purpose of the Study:
- To systematically review current evidence on AI-driven methodologies for optimizing Ozempic-based T2D treatment.
- To explore the synergistic potential of AI and semaglutide in managing type 2 diabetes.
Main Methods:
- A systematic review of 18 peer-reviewed articles was conducted.
- Analysis focused on four key themes: patient stratification, AI-enhanced imaging, risk assessment, and personalized dosing.
- Thematic clusters identified AI applications in body composition, cardiovascular/metabolic risk, and dosage optimization.
Main Results:
- AI-optimized approaches demonstrated superior results in HbA1c reduction, weight loss, and cardiovascular benefits compared to fixed-dose regimens.
- AI integration facilitates personalized treatment, improving adverse event mitigation.
- A framework for AI-Ozempic integration involving continuous data feedback loops was proposed.
Conclusions:
- AI holds significant potential to revolutionize semaglutide therapy for T2D.
- Further research is required, including large-scale RCTs, longer follow-up periods, and explainable AI models.
- Addressing regulatory validation and clinical implementation strategies is crucial for widespread adoption.
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