46 citations · 95 across the 44 of their papers we have counts for
7 papers · 1 filter
AI Can Be Easily Persuaded in Clinical Decision Making
Jiayuan Zhu, Jiazhen Pan, Fenglin Liu +2
As AI becomes increasingly integrated into clinical practice, it is playing a growing role in medical decision making. Medicine, however, is a high stakes and evidence based field,…
J-CoT: Chain-of-Thought in J-Space
Junde Wu, Jiayuan Zhu, Fengling Liu +2
Chain-of-thought prompting improves language-model reasoning by carrying intermediate states across successive computation steps. However, relying on natural language as the only r…
Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance
Tianming Du, Peijie Yu, Sihan Shang +12
The most plausible near-term role of medical LLMs is to assist rather than replace physicians, yet current evaluations often test isolated capabilities: clinical knowledge, EHR sys…
Dynamic Decision Learning: Test-Time Evolution for Abnormality Grounding in Rare Diseases
Jun Li, Mingxuan Liu, Jiazhen Pan +4
Clinical abnormality grounding for rare diseases is often hindered by data scarcity, making supervised fine-tuning impractical and single-pass inference highly unstable. We propose…
Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations
Johannes Moll, Markus Graf, Tristan Lemke +7
Vision-language models (VLMs) often produce chain-of-thought (CoT) explanations that sound plausible yet fail to reflect the underlying decision process, undermining trust in high-…
Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL
Che Liu, Haozhe Wang, Jiazhen Pan +6
Improving performance on complex tasks and enabling interpretable decision making in large language models (LLMs), especially for clinical applications, requires effective reasonin…
Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning
Jiayuan Zhu, Jiazhen Pan, Yuyuan Liu +2
The severe shortage of medical doctors limits access to timely and reliable healthcare, leaving millions underserved. Large language models (LLMs) offer a potential solution but st…