We present GluBox, a multimodal forecasting system that leverages continuous glucose monitoring (CGM) as a core wearable sensing modality, together with behavioral and clinical data that influence blood glucose patterns, to predict long-term blood glucose patterns in individuals with type 1 diabetes while prioritizing clinically consequential errors.
This pilot study examined whether pre-stress music listening alters physiological and psychological responses. Thirty participants either listened to 10 minutes of self-selected relaxing music or quietly read before a modified Trier Social Stress Test.
We propose Artificial Intelligence for Modeling and Explaining Neonatal Health (AIMEN), a deep learning framework that predicts adverse labor outcomes from risk factors and provides the model's reasoning.
We present a structured overview of methods, challenges, and solutions, aiming to support researchers and practitioners in developing reliable and explainable AI solutions for digital health. This paper is further enriched with detailed discussions of the contributions toward robustness and explainability in digital health, the development of trustworthy AI systems in the era of LLMs, and various evaluation metrics for measuring trust and related parameters such as validity, fidelity, and diversity.
A comprehensive review of AI-powered wearable biosensors emphasizing how machine learning and edge AI enable real-time health monitoring and personalized care, with insights on digital twins, LLMs, and challenges in privacy, scalability, and clinical integration.
We developed GlucoLens, that takes sensor-driven inputs and uses advanced data processing, large language models, and explainable machine learning models to predict postprandial AUC and hyperglycemia from diet, physical activity, and recent glucose patterns.