8 citations · 30 across the 34 of their papers we have counts for
9 papers · 1 filter
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…
RareLens: Towards End-to-End Rare Disease Care via Aligning Divergent Large Language Model Reasoning
Xi Chen, Hongru Zhou, Shiyu Feng +24
Rare diseases represent one of the most challenging settings for clinical decision-making, where heterogeneous presentations, sparse evidence and limited expertise create persisten…
Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models
Shengyi Hua, Kangzhe Hu, Conghui He +2
Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that assumes complete information…
DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling
Xizhuo Zhang, Yannian Gu, Zhongzhen Huang +2
Computed tomography (CT) plays a crucial role in medical diagnosis, but minimizing radiation exposure while maintaining image quality remains a critical challenge. Low-dose CT (LDC…
UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors
Zhiwen Yang, Yang Zhou, Haowei Chen +4
Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter signifi…
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…
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…