Nghia Duong-Trung

Alzheimer’s disease (AD) is a leading cause of dementia worldwide, and early detection plays a critical role in enabling timely care and improving quality of life. While deep learning models based on electroencephalography (EEG) microstate representations show strong potential for early AD detection, their complex temporal behaviour generally makes decisions difficult for humans to understand and rely on. This paper presents an explainable approach for temporal EEG microstate classifiers, which helps users understand how brain dynamics contribute to individual predictions. The proposed method produces time-aligned, microstate-aware explanations that reveal which temporal segments and microstate patterns influence AD and normal control decisions. Developed based on a fixed, pre-trained microstate-based dementia detection model across multiple EEG datasets, the approach improves the transparency and interpretability of AI decisions. By translating model behaviour into meaningful temporal brain patterns, this work advances a human-centred approach towards trustworthy AI for EEG-based clinical decision-making for more precise outcomes.

Background: 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. Objective: The objective of this systematic review is to examine current evidence of AI-driven methods to optimize Ozempic-based T2D therapy. Methods: 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. Results: 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. Conclusions: 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.

BACKGROUND 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. OBJECTIVE The objective of this systematic review is to examine current evidence of AI-driven methods to optimize Ozempic-based T2D therapy. METHODS 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. RESULTS 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. CONCLUSIONS 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. CLINICALTRIAL