arXiv:2606. 20164v1 Announce Type: cross Abstract: Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical questions.
By Aueaphum Aueawatthanaphisut
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
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
The paper reviews how Large Language Models (LLMs) are being adapted for medical reasoning, moving beyond single-step answers to systems that can systematically, transparently, and verifiably reason. It introduces a taxonomy of enhancement techniques, split into training-time methods such as supervised fine‑tuning and reinforcement learning, and test-time methods like prompt engineering and multi‑agent systems. The review examines their application across text, image, and code modalities in key clinical areas—diagnosis, education, and treatment planning—and tracks the shift in evaluation benchmarks from simple accuracy to more nuanced assessments of reasoning quality and visual interpretability.
By Zizhan Ma, Wenxuan Wang, Meidan Ding, Shiyi Zheng, Shengyuan Liu, Jie Liu, Jiaming Ji, Linlin Shen, Yixuan Yuan, Wenting Chen
LingShu is a large-scale, symptom‑centric knowledge graph that bridges Traditional Chinese Medicine (TCM) and modern biomedicine. It contains 17.33 million entity records and 39.47 million relation records, combining 17.19 million semantic triples with 22.29 million contextualized quadruples to encode conditional medical associations. The graph integrates data from electronic medical records, TCM texts, biomedical ontologies, and curated knowledge bases, and is supported by a web platform offering visualization, reasoning, and evidence‑grounded question answering.
By Rui Hua, Zixin Shu, Kai Chang, Dengying Yan, Jianan Xia, Hui Zhu, Shujie Song, Shurui Yang, Tongxin Wang, Yue Yin, Yu Wei, Lijuan Pei, Yunhui Hu, Hao Xu, Mingzhong Xiao, Xiaodong Li, Haibin Yu, Runshun Zhang, Wenjia Wang, Baoyan Liu, Xuezhong Zhou
DeepTCM1.0 is a multi‑expert AI agent built on the DeepSeek V3.2 large language model, designed to integrate classical traditional Chinese medicine (TCM) theory with modern life sciences for mechanistic analysis of TCM compound formulas. The framework employs a three‑tier collaborative architecture and a three‑round iterative quality‑control workflow, simulating 11 interdisciplinary intelligent agents. It was validated on the Guizhi Decoction, with performance evaluated through double‑blind five‑dimensional scoring, ICC reliability testing, Mann‑Whitney U tests, and effect size analysis across 100 independent assessments by four large language models.
By Wenxin Duan, Hanwei Wang, Zhongying Peng, Zhonghua Lu, Jiayi An, Fan Song, Yong Liang