AI-Enabled Personalization of Semaglutide Therapy in Type 2 Diabetes: Systematic Review With an Integration Framework

TL;DR

This paper reviews how AI can personalize semaglutide therapy for type 2 diabetes, improving treatment outcomes through tailored approaches.

Contribution

The study introduces a novel integration framework for AI in semaglutide therapy and identifies key areas for future research.

Findings

AI-based approaches outperformed standard fixed-dose regimens in glycemic control and weight loss.

Four thematic clusters were identified: patient stratification, imaging, risk assessment, and personalized dosing.

The proposed framework includes continuous data collection and real-time feedback for treatment optimization.

Abstract

Type 2 diabetes mellitus (T2D) is a rapidly growing global health concern requiring innovative treatment methods. Ozempic (semaglutide), a glucagon-like peptide-1 receptor agonist, has proven consistent effectiveness in lowering blood glucose levels, 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, and medical imaging into practical insights for efficient, tailored, and customized treatment plans.

The objective of this systematic review is to examine current evidence of AI-driven methods to optimize Ozempic-based T2D therapy.

A total of 18 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, such as glycated hemoglobin reduction, weight loss, cardiovascular benefits, 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.

Significant gaps remain a persistent challenge, including the need for large-scale randomized controlled trials, longer follow-up periods, explainable AI 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.

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