arXiv:2607. 08257v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on isolated psychiatric tasks, including dialogue, diagnosis, and treatment planning, yet existing benchmarks rarely simulate complete psychiatric clinical encounters.
By Yuming Yang, Xiao Sun, Yuanwei Zou, Zhengxiao Wu, Yun Chen, Jiang Zhong, Haoyang Zeng, Jingwang Huang, Kaiwen Wei
arXiv:2606. 13220v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as interactive assistants for technical problem solving.
By Fabrizio Marozzo, Pietro Li\`o
arXiv:2603.03677v2 Announce Type: replace-cross
Abstract: Psychiatric consultation requires agents to elicit discriminative evidence, map uncertain narratives to diagnostic criteria, and decide when...
By Guoyi Li, Shihao Xu, Jiatong Ma, Zhongjiang Yao, Yunyun Han, Jianhua Chen, Yafeng Deng
Large language models (LLMs) are increasingly used as interactive assistants for technical problem solving. However, when users provide incomplete descriptions or plausible but unverified explanations, LLMs may prematurely align with these assumptions and propose solutions before collecting sufficient evidence.
The study introduces a scaffolding-oriented multi-agent Large Language Model (LLM) AI Standardized Patient (AI-SP) platform designed to train medical students in patient interviews. In a randomized controlled trial with 100 students, the multi-agent system—comprising a patient agent, a Socratic tutor agent, and a turn-level evaluator—did not improve diagnostic accuracy but significantly enhanced overall OSCE scores, especially in communication, empathy, and history-taking. The authors also release a richly annotated dataset to support further research in AI-supported clinical reasoning training.
By Luming Yang, Haoxian Liu, Siqing Li, Rong Jia, Yue Xiao, Guanhua Chen, Li Lu
arXiv:2602.01995v2 Announce Type: replace
Abstract: Conversational diagnosis requires multi-turn history-taking, where an agent asks clarifying questions to refine differential diagnoses under incomp...
By Jeongmoon Won, Seungwon Kook, Yohan Jo