8 citations · 11 across the 18 of their papers we have counts for
7 papers · 1 filter
MedCEG: Reinforcing Verifiable Medical Reasoning with Critical Evidence Graph
Linjie Mu, Yannian Gu, Zhongzhen Huang +3
Large language models with reasoning capabilities have demonstrated impressive performance across a wide range of domains. In clinical applications, a transparent, step-by-step rea…
CP-Env: Evaluating Large Language Models on Clinical Pathways in a Controllable Hospital Environment
Yakun Zhu, Zhongzhen Huang, Qianhan Feng +5
Medical care follows complex clinical pathways that extend beyond isolated physician-patient encounters, emphasizing decision-making and transitions between different stages. Curre…
UCAgents: Unidirectional Convergence for Visual Evidence Anchored Multi-Agent Medical Decision-Making
Qianhan Feng, Zhongzhen Huang, Yakun Zhu +2
Vision-Language Models (VLMs) show promise in medical diagnosis, yet suffer from reasoning detachment, where linguistically fluent explanations drift from verifiable image evidence…
Radiologist Copilot: An Agentic Framework Orchestrating Specialized Tools for Reliable Radiology Reporting
Yongrui Yu, Zhongzhen Huang, Linjie Mu +2
In clinical practice, radiology reporting is an essential yet complex, time-intensive, and error-prone task, particularly for 3D medical images. Existing automated approaches based…
Elicit and Enhance: Advancing Multimodal Reasoning in Medical Scenarios
Zhongzhen Huang, Linjie Mu, Yakun Zhu +3
Effective clinical decision-making depends on iterative, multimodal reasoning across diverse sources of evidence. The recent emergence of multimodal reasoning models has significan…
DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models
Yakun Zhu, Zhongzhen Huang, Linjie Mu +6
The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, includin…
MMXU: A Multi-Modal and Multi-X-ray Understanding Dataset for Disease Progression
Linjie Mu, Zhongzhen Huang, Shengqian Qin +3
Large vision-language models (LVLMs) have shown great promise in medical applications, particularly in visual question answering (MedVQA) and diagnosis from medical images. However…