1 citations · 3 across the 7 of their papers we have counts for
7 papers
SafeMed-R1: Clinician-Audited Safety and Ethics Alignment for Medical Large Language Models
Chao Ding, Mouxiao Bian, Tianbin Li +12
Large language models(LLMs) increasingly match expert performance on licensing examinations, yet routine clinical use remains limited because governance requires auditable reasonin…
IBISAgent: Reinforcing Pixel-Level Visual Reasoning in MLLMs for Universal Biomedical Object Referring and Segmentation
Yankai Jiang, Qiaoru Li, Binlu Xu +6
Recent research on medical MLLMs has gradually shifted its focus from image-level understanding to fine-grained, pixel-level comprehension. Although segmentation serves as the foun…
Multi-Agent Intelligence for Multidisciplinary Decision-Making in Gastrointestinal Oncology
Rongzhao Zhang, Junqiao Wang, Shuyun Yang +25
Multimodal clinical reasoning in the field of gastrointestinal (GI) oncology necessitates the integrated interpretation of endoscopic imagery, radiological data, and biochemical ma…
Beyond Knowledge to Agency: Evaluating Expertise, Autonomy, and Integrity in Finance with CNFinBench
Jinru Ding, Chao Ding, Yidong Jiang +9
As large language models (LLMs) become high-privilege agents in risk-sensitive settings, they introduce systemic threats beyond hallucination, where minor compliance errors can cau…
MedBench v4: A Robust and Scalable Benchmark for Evaluating Chinese Medical Language Models, Multimodal Models, and Intelligent Agents
Jinru Ding, Lu Lu, Chao Ding +15
Recent advances in medical large language models (LLMs), multimodal models, and agents demand evaluation frameworks that reflect real clinical workflows and safety constraints. We…
Benchmarking Ethical and Safety Risks of Healthcare LLMs in China-Toward Systemic Governance under Healthy China 2030
Mouxiao Bian, Rongzhao Zhang, Chao Ding +2
Large Language Models (LLMs) are poised to transform healthcare under China's Healthy China 2030 initiative, yet they introduce new ethical and patient-safety challenges. We presen…
Building a Human-Verified Clinical Reasoning Dataset via a Human LLM Hybrid Pipeline for Trustworthy Medical AI
Chao Ding, Mouxiao Bian, Pengcheng Chen +11
Despite strong performance in medical question-answering, the clinical adoption of Large Language Models (LLMs) is critically hampered by their opaque 'black-box' reasoning, limiti…