7 papers
MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning
Shengyuan Liu, Liuxin Bao, Qi Yang +6
Medical image segmentation is evolving from task-specific models toward generalizable frameworks. Recent research leverages Multi-modal Large Language Models (MLLMs) as autonomous…
EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis
Shengyuan Liu, Boyun Zheng, Wenting Chen +5
Endoscopic procedures are essential for diagnosing and treating internal diseases, and multi-modal large language models (MLLMs) are increasingly applied to assist in endoscopy ana…
Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications
Wenxuan Wang, Zizhan Ma, Meidan Ding +8
The proliferation of Large Language Models (LLMs) in medicine has enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, a…
RadFabric: Agentic AI System with Reasoning Capability for Radiology
Wenting Chen, Yi Dong, Zhaojun Ding +14
Chest X ray (CXR) imaging remains a critical diagnostic tool for thoracic conditions, but current automated systems face limitations in pathology coverage, diagnostic accuracy, and…
TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Shengyuan Liu, Wenting Chen, Boyun Zheng +3
Tumor data synthesis offers a promising solution to the shortage of annotated medical datasets. However, current approaches either limit tumor diversity by using predefined masks o…
A Survey of LLM-based Agents in Medicine: How far are we from Baymax?
Wenxuan Wang, Zizhan Ma, Zheng Wang +5
Large Language Models (LLMs) are transforming healthcare through the development of LLM-based agents that can understand, reason about, and assist with medical tasks. This survey p…