collaborators

7 papers

cs.CV2026

Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development

Zhongying Deng, Cheng Tang, Ziyan Huang +124

Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…

cs.CL2026

Dental-TriageBench: Benchmarking Multimodal Reasoning for Hierarchical Dental Triage

Ziyi He, Yushi Feng, Shuangyu Yang +7

Dental triage is a safety-critical clinical routing task that requires integrating multimodal clinical information (e.g., patient complaints and radiographic evidence) to determine…

cs.HC2026

Augmenting Clinical Decision-Making with an Interactive and Interpretable AI Copilot: A Real-World User Study with Clinicians in Nephrology and Obstetrics

Yinghao Zhu, Dehao Sui, Zixiang Wang +13

Clinician skepticism toward opaque AI hinders adoption in high-stakes healthcare. We present AICare, an interactive and interpretable AI copilot for collaborative clinical decision…

cs.AI2025

MedMMV: A Controllable Multimodal Multi-Agent Framework for Reliable and Verifiable Clinical Reasoning

Hongjun Liu, Yinghao Zhu, Yuhui Wang +4

Recent progress in multimodal large language models (MLLMs) has demonstrated promising performance on medical benchmarks and in preliminary trials as clinical assistants. Yet, our…

cs.AI2025

ConfAgents: A Conformal-Guided Multi-Agent Framework for Cost-Efficient Medical Diagnosis

Huiya Zhao, Yinghao Zhu, Zixiang Wang +3

The efficacy of AI agents in healthcare research is hindered by their reliance on static, predefined strategies. This creates a critical limitation: agents can become better tool-u…

cs.AI2025

HealthFlow: A Self-Evolving AI Agent with Meta Planning for Autonomous Healthcare Research

Yinghao Zhu, Yifan Qi, Zixiang Wang +8

The rapid proliferation of scientific knowledge presents a grand challenge: transforming this vast repository of information into an active engine for discovery, especially in high…