NV-Reason-CT is a generative vision‑language model designed for chest and abdominal CT analysis that preserves native 3D visual encoding and incorporates radiologist‑guided reasoning. The system couples a 3D vision transformer with a language model, feeding all visual tokens and their 3D coordinates directly into language decoding to maintain volumetric spatial information. Trained on a curated corpus of about 550,000 multimodal instruction examples, the model supports abnormality classification, report generation, and interactive reasoning, achieving strong performance on CT benchmarks and reducing expert interpretation time by 50%.
By Andriy Myronenko, Dong Yang, Yucheng Tang, Baris Turkbey, Benjamin Simon, Stephanie Harmon, Rikhil Makwana, Mariam Aboian, Sena Azamat, Ibrahim Ethem Hamamci, Sezgin Er, Bjoern Menze, Marc Edgar, Yufan He, Pengfei Guo, Daguang Xu
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
arXiv:2604.16729v2 Announce Type: replace-cross
Abstract: State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation r...
By Ayhan Can Erdur, Daniel Scholz, Jiazhen Pan, Benedikt Wiestler, Daniel Rueckert, Jan C. Peeken
arXiv:2604. 15231v2 Announce Type: replace Abstract: Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT).
By M\'elanie Roschewitz, Kenneth Styppa, Yitian Tao, Jiwoong Sohn, Jean-Benoit Delbrouck, Benjamin Gundersen, Nicolas Deperrois, Christian Bluethgen, Julia E. Vogt, Bjoern Menze, Farhad Nooralahzadeh, Michael Krauthammer, Michael Moor
MultiViewDx is a physician‑validated multimodal instruction dataset that links medical imaging studies with patient context and normalizes heterogeneous reports into an evidence‑linked workflow (evidence → findings → differential discussion → diagnosis). The dataset covers a wide range of imaging modalities and uses a unified image‑text retriever to ensure that instruction synthesis is grounded in source‑supported evidence. Fine‑tuned models on MultiViewDx achieve the highest average accuracy on four MedVQA benchmarks and receive the strongest overall rating on JAMA Clinical Challenge cases, with ablations confirming the importance of case‑level multi‑view organization and evidence‑linked reasoning.
By Junda Wang, Zonghai Yao, Yujan Ting, Eric Z. Chen, Hieu Tran, Hong Yu, Weijing Huang, Terrence Chen
Lingshu is a medical‑specialized multimodal large language model that addresses key limitations of existing medical MLLMs, such as narrow knowledge coverage, hallucinations, and weak reasoning. The authors curate a comprehensive dataset combining medical imaging, texts, and general‑domain data, then train Lingshu in multiple stages to embed medical expertise and improve task performance. They also introduce MedEvalKit, a unified evaluation framework, and demonstrate that Lingshu outperforms current open‑source multimodal models on multimodal QA, text‑based QA, and medical report generation.
By Weiwen Xu, Hou Pong Chan, Long Li, Mahani Aljunied, Ruifeng Yuan, Jianyu Wang, Chenghao Xiao, Guizhen Chen, Chaoqun Liu, Zhaodonghui Li, Yu Sun, Junao Shen, Chaojun Wang, Jie Tan, Deli Zhao, Tingyang Xu, Hao Zhang, Yu Rong
LiteMedCoT-VL is a parameter‑efficient pipeline that transfers chain‑of‑thought reasoning from a 235B teacher model to a 2B student model using LoRA fine‑tuning on explanation‑enriched data. The approach enables a compact vision‑language model to perform medical visual question answering without relying on image captions, achieving 64.9% accuracy on the PMC‑VQA benchmark—an 11‑point improvement over the zero‑shot Qwen3‑VL‑4B baseline. Visual grounding analysis confirms that the model bases its predictions on image content rather than textual priors.
By Runze Ma, Shunbo Jia, Haonan Lyu, Guo Liu, Caizhi Liao
arXiv:2505. 14107v5 Announce Type: replace-cross Abstract: The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, including those arising in complex clinical scenarios.
By Yakun Zhu, Zhongzhen Huang, Linjie Mu, Yutong Huang, Wei Nie, Jiaji Liu, Shaoting Zhang, Pengfei Liu, Xiaofan Zhang
arXiv:2606. 14766v1 Announce Type: cross Abstract: Autonomous medical and robotic systems increasingly rely on intelligent perception and reasoning capabilities to interpret visual data and support clinical decision making.
By Hamza Riaz, Arham Haroon, Maha Baig, Muhammad Dawood Rizwan, Muhammad Naseer Bajwa, Muhammad Moazam Fraz
The paper introduces UR$^{2}$-MLLM, an uncertainty‑aware multimodal large language model that dynamically revisits uncertain image regions during radiology report generation. It incorporates an uncertainty perception module trained on a specialized dataset, builds a multimodal reasoning trajectory with a detect‑and‑copy mechanism to guide revisits, and refines this behavior using a visual grounding reward via reinforcement learning. Experiments on MIMIC‑CXR and IU‑Xray demonstrate state‑of‑the‑art performance, underscoring the importance of visual revisit reasoning for reliable, clinically aligned reports.
By Yucheng Chen, Yang Yu, Jiazhou Zhou, Yufei Shi, Yongying Lan, Yichi Zhang, Liyi Li, Si Yong Yeo
arXiv:2606. 06407v1 Announce Type: cross Abstract: Medical imaging artificial intelligence has achieved strong performance in isolated image interpretation, but remains poorly aligned with radiological practice, where diagnosis and follow-up rely on comparison across prior studies and analogous reference cases.
By Tengfei Zhang, Ziheng Zhao, Lisong Dai, Xiaoman Zhang, Pengcheng Qiu, Ya Zhang, Yanfeng Wang, Weidi Xie
The paper introduces an uncertainty‑aware clinical knowledge graph for chest X‑ray device reasoning, capturing device instances, tip estimates, placement assessments, provenance, report events, and temporal links as interconnected evidence. The graph builder processes 30,083 studies from 3,255 patients, producing 914,632 evidence nodes and 884,549 typed relationships, while preserving detailed uncertainty and provenance information for each predicted device. The authors also outline typed data contracts, uncertainty representations, abstention rules, report‑image grounding, and longitudinal query mechanisms, though the current analysis is post‑hoc descriptive and does not yet demonstrate clinical utility.
By Harshil Lodhiya