arXiv:2606. 12699v1 Announce Type: cross Abstract: Type 2 Diabetes (T2D) poses an increasing global health threat, demanding effective glycemic assessment to support personalized and improved diabetes care.
arXiv:2609.08772v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly being investigated for physiological time-series prediction, yet their effectiveness may depend not only...
The paper examines how the representation of physiological data affects the performance of large language models (LLMs) in predicting post‑meal blood glucose events for people with type 1 diabetes. Using the OhioT1DM dataset, the authors compare zero‑shot and few‑shot prompt‑based LLMs across 30, 60, and 90‑minute horizons, varying the textual encoding of glucose readings, derived descriptors, and contextual variables such as insulin, meals, carbs, and activity. Results show that while conventional supervised models excel at hyperglycemia prediction, certain prompt‑based LLM configurations outperform them for hypoglycemia, and that the way data is presented to the model is a key determinant of success, with added context not consistently improving outcomes.
arXiv:2601. 05353v2 Announce Type: replace Abstract: Accurate blood glucose forecasting using continuous glucose monitoring (CGM) data can support the early prediction of dysglycemic risk.
arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.
arXiv:2606. 10120v1 Announce Type: cross Abstract: Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing recommendations that are difficult to follow or not impactful.
arXiv:2507.14077v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) algorithms are a critical part of state-of-the-art digital health technology for diabetes management. Yet, acces...
WearableQA is a new benchmark that tests AI systems on health reasoning using real-world wearable data from 200 users, each with up to 500 days of daily measurements. It contains 4,084 ten‑option multiple‑choice questions derived from wearable time series, blood biomarkers, and demographics, and is organized into 16 question types that distinguish data‑driven computation from physiological interpretation and single‑signal from cross‑signal reasoning. Evaluation of 14 large language models shows wide performance gaps, indicating that the benchmark remains challenging and useful for diagnosing model capabilities.
arXiv:2607. 19006v1 Announce Type: new Abstract: Accurate forecasting of blood glucose concentration is key in the management of Type 1 Diabetes, facilitating early detection of adverse glycemic events and supporting timely therapeutic interventions.
arXiv:2606. 24145v1 Announce Type: new Abstract: Large language models (LLMs) can produce clinically fluent recommendations for type 2 diabetes while failing to satisfy guideline constraints or explicitly justify lifestyle-related glycemic claims.
arXiv:2604. 23954v2 Announce Type: replace Abstract: Artificial Intelligence (AI) and Machine Learning (ML) models used in clinical settings are increasingly deployed to support clinical decision-making.
arXiv:2607. 21117v1 Announce Type: cross Abstract: Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes management, particularly for type 1 diabetes.
arXiv:2606. 18640v1 Announce Type: new Abstract: Glucose forecasting algorithms are an important aspect of glycemic control management in type 1 diabetes.