Tootfinder

MExplore: an entity-based visual analytics approach for medical expertise acquisition
Xiao Pang, Yan Huang, Chang Liu, JiYuan Liu, MingYou Liu
https://arxiv.org/abs/2507.12337

MExplore: an entity-based visual analytics approach for medical expertise acquisition
Acquiring medical expertise is a critical component of medical education and professional development. While existing studies focus primarily on constructing medical knowledge bases or developing learning tools based on the structured, private healthcare data, they often lack methods for extracting expertise from unstructured medical texts. These texts constitute a significant portion of medical literature and offer greater flexibility and detail compared to structured data formats. Furthermore…

Explicit construction of benign subgroup for Higman's reversing operation
V. H. Mikaelian
https://arxiv.org/abs/2506.12442 https://

Explicit construction of benign subgroup for Higman's reversing operation
For the Higman reversing operation $ρ$ and for a set of integer-valued functions $\mathcal X$ the following has been proved. Let the subgroup $A_{\mathcal X}$ be benign in the free group $F$, let the respective finitely presented overgroup $K_{\mathcal X}$ with its finitely generated subgroup $L_{\mathcal X}$ be given for $A_{\mathcal X}$ explicitly, and let the set $\mathcal Y = ρ\mathcal X$ be obtained from $\mathcal X$ by the reversing operation $ρ$. Then $A_{\mathcal Y}$ also is benign i…

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning
Xiaotian Zhang, Yuan Wang, Zhaopeng Feng, Ruizhe Chen, Zhijie Zhou, Yan Zhang, Hongxia Xu, Jian Wu, Zuozhu Liu
https://arxiv.org/abs/2506.12307

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning
Medical Question-Answering (QA) encompasses a broad spectrum of tasks, including multiple choice questions (MCQ), open-ended text generation, and complex computational reasoning. Despite this variety, a unified framework for delivering high-quality medical QA has yet to emerge. Although recent progress in reasoning-augmented large language models (LLMs) has shown promise, their ability to achieve comprehensive medical understanding is still largely unexplored. In this paper, we present Med-U1, …

Afrinic: Gericht stoppt Vorstandswahl 
Die aktuelle Wahl neuer Afrinic-Vorstände ist gerichtlich gestoppt. Ein Providerverband aus Tansania sieht die Rechte seiner Mitglieder verletzt. 
https://www.

South Africa Built a Medical Research Powerhouse. Trump Cuts Have Demolished It. (Stephanie Nolen/New York Times)
https://www.nytimes.com/2025/06/17/health/south-africa-medical-research-trump.html?unlocked_article_code=1.Pk8.ZEjN.PrSZsqsRX0m6&smid=nytcore-ios-share&referringSource=articleShare
http://www.memeorandum.com/250617/p46#a250617p46

Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
Janet Wang, Yunbei Zhang, Zhengming Ding, Jihun Hamm
https://arxiv.org/abs/2506.12323

Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
Paucity of medical data severely limits the generalizability of diagnostic ML models, as the full spectrum of disease variability can not be represented by a small clinical dataset. To address this, diffusion models (DMs) have been considered as a promising avenue for synthetic image generation and augmentation. However, they frequently produce medically inaccurate images, deteriorating the model performance. Expert domain knowledge is critical for synthesizing images that correctly encode clin…

Med-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained Reflection
Zongxian Yang, Jiayu Qian, Zegao Peng, Haoyu Zhang, Zhi-An Huang
https://arxiv.org/abs/2506.13793

Med-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained Reflection
Large reasoning models have recently made significant strides in mathematical and code reasoning, yet their success has not transferred smoothly to the medical domain. While multiple factors contribute to this disparity, a critical issue is the inadequate focus on the quality of intermediate reflection steps, which is particularly crucial in high-stakes medical scenarios. To address this challenge, we propose Med-REFL, a \underline{\textbf{Med}}ical \underline{\textbf{R}}easoning \underline{\te…

Nabla, which offers an AI copilot for doctors and other medical staff, raised a $70M Series C led by HV Capital and says it now supports 85K clinicians (Judy Rider/Crunchbase News)
https://news.crunchbase.com/ai/nabla-ai-copilot-agents-healthcare-fundin…

Nabla Lands $70M To Build AI Agents In Healthcare Settings
Paris-based Nabla, which has developed an AI copilot for doctors and other medical staff, announced Tuesday that it has raised $70 million in a Series C funding led by Germany’s HV Capital.

📉 Home, but not safe: How poor housing is harming health
#housing

Home, but not safe: How poor housing is harming health
When Israelis think about health, they may picture hospitals, doctors, or the rising cost of prescription drugs. But a new integrative study by Jordan Hannink Attal (Hebrew University and the University of Melbourne) and Prof. Yehuda Neumark (Hebrew University) suggests the most important factor may be much closer to home—literally.

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Daniya Najiha Abdul Kareem, Abdul Hannan, Mubashir Noman, Jean Lahoud, Mustansar Fiaz, Hisham Cholakkal
https://arxiv.org/abs/2506.12208

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolutional neural networks (CNNs) and transformer-based models have been extensively studied to enhance receptive fields and improve medical image segmentation task. However, they often struggle to capture complex cellular and tissue structures in challenging scenarios such as background clutter and object overlap. Moreove…