DiabAgent introduces a research suite for agentic physiological control in type 1 diabetes, comprising the APC framework, DiabetSim simulator, and SweetBench benchmark. The APC framework uses medical-device tools and ongoing observations to infer patient states, accumulate experience, and select treatment actions. In virtual testing with DiabetSim, APC outperforms the open-source baseline OpenAPS, achieving a COGI of 73.5 versus 71.2, and ablation studies show that individual adaptation and contextual information improve glucose control.
arXiv:2608. 13581v1 Announce Type: cross Abstract: Personalized glucose regulation remains a central yet unresolved challenge in precision nutrition, as postprandial glucose response varies substantially across individuals.
The study examines how temporal resampling (binning) of irregular clinical data affects the safety and performance of offline reinforcement learning (ORL) models in healthcare. By analyzing MIMIC‑IV insulin infusion records, the authors show that binning can reverse causal order and obscure critical events. In simulated trials with virtual type‑1 diabetes patients, ORL models trained on binned data performed up to 60% worse than those trained on unprocessed data, yet retrospective evaluation on resampled data overestimated their returns.
arXiv:2607. 14126v1 Announce Type: new Abstract: Type 1 Diabetes (T1D) is a chronic, life-threatening autoimmune condition characterized by the complete destruction of insulin-producing pancreatic beta cells.
arXiv:2608.31128v1 Announce Type: new Abstract: Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and ci...
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:2605. 29483v2 Announce Type: replace Abstract: Wearable devices enable continuous monitoring of physiological signals such as ECG and PPG, but existing mHealth systems are largely limited to task-specific prediction pipelines or reactive question answering over static summaries.
arXiv:2606. 19481v1 Announce Type: new Abstract: Offline reinforcement learning (ORL) offers the potential to improve the quality of clinical decision-making using historical electronic health record (EHR) data.
arXiv:2503. 19158v3 Announce Type: replace Abstract: Type 1 Diabetes (T1D) management is a complex task due to many variability factors.
As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous and holistic evaluation is essential for measuring progress toward real-world healthcare applications. We introduce HealthAgentBench, a suite of 54 agentic healthcare tasks across 7 categories each with its unique environment.
ClinicalReTrial is a multi‑agent AI system that treats clinical trial protocol optimization as an iterative redesign problem on textual documents. It combines failure diagnosis, safety‑aware modifications, and candidate evaluation within a closed‑loop, reward‑driven framework, using a predictive model as a simulation environment for low‑cost, dense feedback. The system achieves a 56.7% conversion of failed protocols to predicted successes, with a 7.4% average success probability increase at a negligible cost, and its redesign patterns align with real‑world expert changes.
arXiv:2606. 18640v1 Announce Type: new Abstract: Glucose forecasting algorithms are an important aspect of glycemic control management in type 1 diabetes.
SSMCGM-Stream is a streaming state‑space model designed for personalized diabetes management. It updates a hidden state with each new glucose reading, enabling constant‑time forecasting, physiologically structured counterfactual reasoning, and interpretable forecasts via hidden‑state attribution. The model, evaluated on the T1DEXI cohort, matches strong neural baselines in accuracy while significantly lowering computational cost, making continuous on‑device decision support feasible.