arXiv AI

Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models

arXiv:2511. 14900v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis.

arXiv AI

Jun 11

OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models

arXiv:2606. 12169v1 Announce Type: cross Abstract: High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers.

By Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci, Abeer Badawi, Adibvafa Fallahpour, Arash Afkanpour, Leonid Sigal, Ali Etemad, Elham Dolatabadi

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jul 2

RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation

arXiv:2607. 00147v1 Announce Type: new Abstract: Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space.

By Deyang Jiang, Haoran Wu, Ziyi Wang, Yiming Rong, Yunlong Zhao, Ye Jin, Bo Xu

llms rag agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 10

A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding

arXiv:2512. 21414v2 Announce Type: replace-cross Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools.

By Christina Liu, Alan Q. Wang, Joy Hsu, Jiajun Wu, Ehsan Adeli

llms multimodal benchmarks

arXiv AI

Jul 7

Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models

arXiv:2607. 02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that assumes complete information.

By Shengyi Hua, Kangzhe Hu, Conghui He, Xiaofan Zhang, Shaoting Zhang

llms rag agents reinforcement-learning

arXiv AI

Jun 9

MedVision: Benchmarking Quantitative Medical Image Analysis

arXiv:2511. 18676v2 Announce Type: replace-cross Abstract: Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.

By Yongcheng Yao, Yongshuo Zong, Raman Dutt, Yongxin Yang, Sotirios A Tsaftaris, Timothy Hospedales

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Jul 31

Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge

arXiv:2603. 07131v4 Announce Type: replace-cross Abstract: Large Vision Language Models (LVLMs) show immense potential for automated ophthalmic diagnosis.

By Shuai Lu, Meng Wang, Jia Guo, Jiawei Du, Bo Liu, Shengzhu Yang, Weihang Zhang, Huazhu Fu, Huiqi Li

llms rag nlp benchmarks safety

arXiv Machine Learning

Jun 5

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology

arXiv:2606. 06224v1 Announce Type: cross Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology.

By Yanqing Luo (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Julius Hense (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Niklas Preni{\ss}l (Institute of Pathology, Charit\'e Universit\"atsmedizin, Berlin, Germany, Berlin Institute of Health at Charit\'e -- Universit\"atsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charit\'e Digital Clinician Scientist Program, Berlin, Germany), Andreas Mock (Institute of Pathology, Ludwig Maximilian University of Munich, Munich, Germany, Division of Translational Medical Oncology, DKFZ, Heidelberg, Germany, NCT Heidelberg, Heidelberg, Germany, German Cancer Consortium), Klaus-Robert M\"uller (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany, Department of Artificial Intelligence, Korea University, Seoul, Korea, Max-Planck Institute for Informatics, Saarbr\"ucken, Germany), Thomas Schnake (Department of Chemistry, Chemical Physics Theory Group, University of Toronto, Canada, Vector Institute for Artificial Intelligence, Toronto, Canada, Acceleration Consortium, University of Toronto, Canada), Mina Jamshidi Idaji (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany)

safety

arXiv AI

Jun 3

ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning

arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).

By Bo-Hong Wang, Baicheng Peng, Ruilin Wang, Jun Bai, Ziyang Song, Yue Li

llms multimodal benchmarks safety

arXiv AI

Jul 20

GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis

arXiv:2607. 15280v1 Announce Type: new Abstract: Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering.

By Shaoting Tan, Ning Liu, Yuntao Du, Shuyue Wei, Wu Shuai, Qian Li, Yanyu Xu, Wei Zhang, Lizhen Cui, Haitao Yuan

llms agents efficiency

arXiv AI

Jun 15

STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction

arXiv:2604. 09737v2 Announce Type: replace-cross Abstract: Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and heterogeneous group difficulty.

By Samah Fodeh, Ganesh Puthiaraju, Elyas Irankhah, Afshan Khan, Sreeraj Ramachandran, Linhai Ma, Srivani Talakokkul, Sarah Schellhorn

llms fine-tuning safety

arXiv AI

Jul 21

Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry

arXiv:2505. 02722v2 Announce Type: replace Abstract: Although large language models (LLMs) have demonstrated impressive reasoning capabilities across general domains, their effectiveness in real-world clinical practice remains limited.

By Junu Kim, Chaeeun Shim, Sungjin Park, Su Yeon Lee, Gee Young Suh, Chae-Man Lim, Seong Jin Choi, Song Mi Moon, Kyoung-Ho Song, Eu Suk Kim, Hong Bin Kim, Sejoong Kim, Chami Im, Dong-Wan Kang, Yong Soo Kim, Hee-Joon Bae, Sung Yoon Lim, Han-Gil Jeong, Edward Choi

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 17

Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation

arXiv:2606. 17958v1 Announce Type: cross Abstract: Semi-supervised medical image segmentation has emerged as a dominant research problem in medical image analysis, mitigating annotation scarcity by leveraging consistency regularization on unlabeled data.

By Yuming Chen, Yuxin Xie, Tao Zhou, Yi Zhou

llms computer-vision benchmarks

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