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Georgia, Medicaid
Hawai’i, MedQuest
Idaho, Medicaid
Illinois, Medical Assistance Program
Indiana, Hoosier Healthwise, Hoosier Care Connect, M.E.D. Works, Health Indiana Plan (HIP), Traditional Medicaid
Iowa, Iowa Health Link
Kansas, KanCare Medical Assistance Program
Kentucky, Medicaid
Louisiana, Bayou Health, Healthy Louisiana
Maine, MaineCare
Maryland, Medical Care
Massachusetts, MassHealth
Michigan, Medical Assistance or MA
Minnesota, Medical Assistance (MA), MinnesotaCare

The ordering of hypertrees and unicyclic hypergraphs by the traces of $\mathcal{A}_{\alpha}$-tensor
Jueru Liu, Lizhu Sun, Changjiang Bu
https://arxiv.org/abs/2507.02650

The ordering of hypertrees and unicyclic hypergraphs by the traces of $\mathcal{A}_α$-tensor
For a real number $α\in[0,1]$ and a $k$-uniform hypergraph $\mathcal{H}$, $\mathcal{A}_α(\mathcal{H})=α\mathcal{D}(\mathcal{H})+(1-α)\mathcal{A}(\mathcal{H})$ is called the $\mathcal{A}_α$-tensor of $\mathcal{H}$, where $\mathcal{D}(\mathcal{H})$ and $\mathcal{A}(\mathcal{H})$ are the degree tensor and adjacency tensor of $\mathcal{H}$, respectively. The sum of the $d$-th powers of all eigenvalues of $\mathcal{A}_α(\mathcal{H})$ is called the $d$-th order $\mathcal{A}_α$-spectral moment …

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Ning Zhu, Xiaochuan Ma, Shaoting Zhang, Guotai Wang
https://arxiv.org/abs/2508.03441

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Cold-Start Active Learning (CSAL) aims to select informative samples for annotation without prior knowledge, which is important for improving annotation efficiency and model performance under a limited annotation budget in medical image analysis. Most existing CSAL methods rely on Self-Supervised Learning (SSL) on the target dataset for feature extraction, which is inefficient and limited by insufficient feature representation. Recently, pre-trained Foundation Models (FMs) have shown powerful f…

Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
Qi Peng, Jialin Cui, Jiayuan Xie, Yi Cai, Qing Li
https://arxiv.org/abs/2508.03038

Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
Large language models (LLMs) have shown great potential in the medical domain. However, existing models still fall short when faced with complex medical diagnosis task in the real world. This is mainly because they lack sufficient reasoning depth, which leads to information loss or logical jumps when processing a large amount of specialized medical data, leading to diagnostic errors. To address these challenges, we propose Tree-of-Reasoning (ToR), a novel multi-agent framework designed to handl…

Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models
Juraj Vladika, Mahdi Dhaini, Florian Matthes
https://arxiv.org/abs/2509.04304 htt…

Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models
The growing capabilities of Large Language Models (LLMs) show significant potential to enhance healthcare by assisting medical researchers and physicians. However, their reliance on static training data is a major risk when medical recommendations evolve with new research and developments. When LLMs memorize outdated medical knowledge, they can provide harmful advice or fail at clinical reasoning tasks. To investigate this problem, we introduce two novel question-answering (QA) datasets derived…

Ketryx, which is building an AI-native compliance platform to speed up medical device development, raised a $39M Series B, bringing its total funding to $55M (Kyt Dotson/SiliconANGLE)
https://siliconangle.com/2025/09/04/ketryx-rai…

Ketryx raises $39M to help medical device makers ship safer products faster - SiliconANGLE
Ketryx raises $39M to help medical device makers ship safer products faster - SiliconANGLE

Mississippi, Mississippi Coordinated Access Network (MississippiCAN)
Missouri, MO HealthNet
Montana, Medicaid
Nebraska, ACCESSNebraska, Nebraska Medical Assistance Program (NMAP)
Nevada, Medicaid
New Hampshire, NH Medicaid, Medical Assistance
New Jersey, NJ Family Care
New Mexico, Centennial Care, Medical Assistance
New York, Medicaid Managed Care
North Carolina, NC Medicaid Managed Care
North Dakota, North Dakota Medicaid Expansion Program
Ohio, Medicaid
Oklahoma, SoonerCare

Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation
Junying Meng, Gangxuan Zhou, Jun Liu, Weihong Guo
https://arxiv.org/abs/2509.04273

Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation
Despite signi cant progress in semi-supervised medical image segmentation, most existing segmentation networks overlook e ective methodological guidance for feature extraction and important prior information from datasets. In this paper, we develop a semi-supervised medical image segmentation framework that e ectively integrates spatial regularization methods and volume priors. Speci cally, our approach integrates a strong explicit volume prior at the image scale and Threshold Dynamics spatia…

An Agentic Model Context Protocol Framework for Medical Concept Standardization
Jaerong Ahn, Andrew Wen, Nan Wang, Heling Jia, Zhiyi Yue, Sunyang Fu, Hongfang Liu
https://arxiv.org/abs/2509.03828

An Agentic Model Context Protocol Framework for Medical Concept Standardization
The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) provides a standardized representation of heterogeneous health data to support large-scale, multi-institutional research. One critical step in data standardization using OMOP CDM is the mapping of source medical terms to OMOP standard concepts, a procedure that is resource-intensive and error-prone. While large language models (LLMs) have the potential to facilitate this process, their tendency toward hallucination ma…

Congress' 'watchdog' is all bark and no bite when it finds law-breaking
Trump illegally froze 1,800 medical research grants, Congress’ watchdog says • Louisiana Illuminator
https://lailluminator.com/2025/08/05/repub/medical-research-grants/