FairGlucose is a 300‑patient CGM cohort balanced across 12 demographic strata, providing 132,480 forecasting samples and 3,945 behavioral events. Benchmarking 33 models on 2‑hour glucose forecasting revealed that population‑level validation masks significant subgroup disparities, with error ratios ranging from 0.8 to 1.4 and T1D patients experiencing 6 mg/dL higher error than T2D. The study shows that these gaps persist across all models, align with clinically hard cases, and vary with input‑length sensitivity, underscoring the need for subgroup‑disaggregated reporting in digital health AI.
By Junjie Luo, Xuzhe Zhi, Rui Han, Abhimanyu Kumbara, Anand K. Iyer, Mansur E. Shomali, Ritu Agarwal, Guodong Gordon Gao
arXiv:2606. 16056v1 Announce Type: new Abstract: Dysglycemia, encompassing both prediabetes and diabetes, affects huge numbers of adults worldwide, yet many of them remain undiagnosed.
By Black Sun, Chenyi Zhang, Kaiyi Ji, Xi Lu
arXiv:2607. 08953v1 Announce Type: new Abstract: Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes.
By Nick Souligne, Isabella Mixton-Garcia, Vignesh Subbian
Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes. This limits practical guidance for selecting fairness strategies, where disparities may arise across intersectional subgroups and across multiple stages of the modeling lifecycle.
arXiv:2604. 16450v2 Announce Type: replace-cross Abstract: Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess demographic attributes independently.
By Nick Souligne, Vignesh Subbian
arXiv:2606. 09860v1 Announce Type: cross Abstract: Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks.
By Xinze Zhang