most citedLeveraging Large Language Model as Simulated Patients for Clinical Education

15 citations · 16 across the 3 of their papers we have counts for

collaborators

5 papers

eess.AS2026

Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding

Zhiyuan Zhu, Yixuan Chen, Yiwen Shao +13

Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for sound localization, spatial rel…

cs.HC2026

Overview of the ClinicalSkillQA 2026 Shared Task on Continuous Perception and Procedural Reasoning in Clinical Skill Assessment

Xiyang Huang, Renxiong Wei, Yihuai Xu +10

This paper presents an overview of the ClinicalSkillQA 2026 shared task, which was organized with the BioNLP Workshop at ACL 2026. The goal of this shared task is to evaluate conti…

cs.CV2026

SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos

Xiyang Huang, Jiawei Lin, Keying Wu +9

Current video benchmarks for multimodal large language models (MLLMs) focus on event recognition, temporal ordering, and long-context recall, but overlook a harder capability requi…

cs.AI20241 cited

MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient

Yanzeng Li, Cheng Zeng, Jinchao Zhang +2

Medical education relies heavily on Simulated Patients (SPs) to provide a safe environment for students to practice clinical skills, including medical image analysis. However, the…

cs.CL202415 cited

Leveraging Large Language Model as Simulated Patients for Clinical Education

Yanzeng Li, Cheng Zeng, Jialun Zhong +3

Simulated Patients (SPs) play a crucial role in clinical medical education by providing realistic scenarios for student practice. However, the high cost of training and hiring qual…