SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening
Department of Electrical and Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）；NeuTigers, Inc.（NeuTigers公司）；AP-HP Avicennes, Bobigny, France（法国AP-HP阿维尼翁医院）；Clinic International Constantine, Constantine, Algeria（阿尔及利亚康斯坦丁国际诊所）
The global rise in type 2 diabetes underscores the need for scalable and cost-effective screening methods. Current diagnosis requires biochemical assays, which are invasive and costly. Advances in consumer wearables have enabled early explorations of machine learning-based disease detection, but prior studies were limited to controlled settings. We present SweetDeep, a compact neural network trained on physiological and demographic data from 285 (diabetic and non-diabetic) participants in the EU and MENA regions, collected using Samsung Galaxy Watch 7 devices in free-living conditions over six days. Each participant contributed multiple 2-minute sensor recordings per day, totaling approximately 20 recordings per individual. Despite comprising fewer than 3,000 parameters, SweetDeep achieves 82.5% patient-level accuracy (82.1% macro-F1, 79.7% sensitivity, 84.6% specificity) under three-fold cross-validation, with an expected calibration error of 5.5%. Allowing the model to abstain on less than 10% of low-confidence patient predictions yields an accuracy of 84.5% on the remaining patients. These findings demonstrate that combining engineered features with lightweight architectures can support accurate, rapid, and generalizable detection of type 2 diabetes in real-world wearable settings.
LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校信息系统与网络安全系）；School of Engineering Medicine, Texas A&M University（德克萨斯农工大学工程医学院）；Department of Family and Community Medicine, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校家庭与社区医学系）
HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care
Mayo Clinic（梅奥诊所）；Imperial College London（伦敦帝国学院）；Imperial Global Singapore（新加坡帝国全球学院）；National University of Singapore（新加坡国立大学）；The University of Manchester（曼彻斯特大学）；Nanyang Technological University（南洋理工大学）
LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）；College of Health Solutions, Arizona State University（健康解决方案学院，亚利桑那州立大学）
With the increasing availability of wearable devices, continuous monitoring of individuals' physiological and behavioral patterns has become significantly more accessible. Access to these continuous patterns about individuals' statuses offers an unprecedented opportunity for studying complex diseases and health conditions such as type 2 diabetes (T2D). T2D is a widely common chronic disease that its roots and progression patterns are not fully understood. Predicting the progression of T2D can inform timely and more effective interventions to prevent or manage the disease. In this study, we hav…
AI procedures joined with wearable gadgets can convey exact transient blood glucose level forecast models. Also, such models can learn customized glucose-insulin elements dependent on the sensor information gathered by observing a few parts of the physiological condition and every day movement of a person. Up to this point, the predominant methodology for creating information driven forecast models was to gather "however much information as could be expected" to help doctors and patients ideally change treatment. The goal of this work was to examine the base information assortment, volume, and…
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography
New York University（纽约大学）；NYU Langone Health（NYU Langone健康中心）；NYU Tandon School of Engineering（NYU塔能工程学院）；Indiana University School of Medicine（印第安纳大学医学院）
Towards Affordable, Non-Invasive Real-Time Hypoglycemia Detection Using Wearable Sensor Signals
Department of Computer Science, Old Dominion University, Norfolk, VA, USA（计算机科学系，旧 Dominion 大学）；Virginia Digital Maritime Center (VDMC), Old Dominion University, Norfolk, VA, USA（弗吉尼亚数字水路中心（VDMC），旧 Dominion 大学）
Use of Continuous Glucose Monitoring with Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes
Department of Genetics, Stanford University（遗传学系，斯坦福大学）；Stanford University（斯坦福大学）；Google Research（谷歌研究）；Systems and Biomedical Engineering Department, Cairo University（系统与生物医学工程系，开罗大学）；Department of International Health, Johns Hopkins Bloomberg School of Public Health（国际健康系，约翰·霍普金斯布劳尔学校公共卫生学院）；Department of Medicine, Stanford University（医学系，斯坦福大学）
REMONI: An Autonomous System Integrating Wearables and Multimodal Large Language Models for Enhanced Remote Health Monitoring
2 Department of Electrical；Computer Engineering University of Waterloo, Waterloo, ON, Canada N2L 3G1 Email；3 College of Computer；Information Sciences Prince Sultan University Email
