8 citations · 11 across the 18 of their papers we have counts for
19 papers
EviDx: Evidence-Aware Active Diagnosis with Scaffolded LLM Agents
Lihang Zeng, Shaoting Zhang, Xiaofan Zhang
Clinical diagnosis is an active evidence-seeking process in which clinicians acquire evidence, update competing hypotheses, and decide when the available evidence is sufficient for…
Human-AI Co-reasoning for Clinical Diagnosis with Evidence-Integrated Language Agent
Zhongzhen Huang, Yan Ling, Hong Chen +7
We present PULSE, a medical reasoning agent that combines a domain-tuned large language model with scientific literature retrieval to support diagnostic decision-making in complex…
CURE: A Multimodal Benchmark for Clinical Understanding and Retrieval Evaluation
Yannian Gu, Zhongzhen Huang, Linjie Mu +3
Multimodal large language models (MLLMs) demonstrate considerable potential in clinical diagnostics, a domain that inherently requires synthesizing complex visual and textual data…
CT-Flow: Orchestrating CT Interpretation Workflow with Model Context Protocol Servers
Yannian Gu, Xizhuo Zhang, Linjie Mu +4
Recent advances in Large Vision-Language Models (LVLMs) have shown strong potential for multi-modal radiological reasoning, particularly in tasks like diagnostic visual question an…
EHRWorld: A Patient-Centric Medical World Model for Long-Horizon Clinical Trajectories
Linjie Mu, Zhongzhen Huang, Yannian Gu +3
World models offer a principled framework for simulating future states under interventions, but realizing such models in complex, high-stakes domains like medicine remains challeng…
MedMCP-Calc: Benchmarking LLMs for Realistic Medical Calculator Scenarios via MCP Integration
Yakun Zhu, Yutong Huang, Shengqian Qin +3
Medical calculators are fundamental to quantitative, evidence-based clinical practice. However, their real-world use is an adaptive, multi-stage process, requiring proactive EHR da…
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…
MeNTi: Bridging Medical Calculator and LLM Agent with Nested Tool Calling
Yakun Zhu, Shaohang Wei, Xu Wang +3
Integrating tools into Large Language Models (LLMs) has facilitated the widespread application. Despite this, in specialized downstream task contexts, reliance solely on tools is i…
MedOdyssey: A Medical Domain Benchmark for Long Context Evaluation Up to 200K Tokens
Yongqi Fan, Hongli Sun, Kui Xue +3
Numerous advanced Large Language Models (LLMs) now support context lengths up to 128K, and some extend to 200K. Some benchmarks in the generic domain have also followed up on evalu…
Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models
Zhongzhen Huang, Kui Xue, Yongqi Fan +5
Large-scale language models (LLMs) have achieved remarkable success across various language tasks but suffer from hallucinations and temporal misalignment. To mitigate these shortc…
Grounded Knowledge-Enhanced Medical Vision-Language Pre-training for Chest X-Ray
Qiao Deng, Zhongzhen Huang, Yunqi Wang +6
Medical foundation models have the potential to revolutionize healthcare by providing robust and generalized representations of medical data. Medical vision-language pre-training h…
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation
Zhongzhen Huang, Linda Wei, Shaoting Zhang +1
Combining images from multi-modalities is beneficial to explore various information in computer vision, especially in the medical domain. As an essential part of clinical diagnosis…
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine
Xiaosong Wang, Xiaofan Zhang, Guotai Wang +17
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and…