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20242026
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cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2025

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…