Effective communication between healthcare providers and patients is crucial to providing high-quality patient care. In this work, we investigate how Doctor-written and AI-generated texts in healthcare consultations can be classified using state-of-the-art embeddings and one-shot classification systems. By analyzing embeddings such as bag-of-words, character n-grams, Word2Vec, GloVe, fastText, and GPT2 embeddings, we examine how well our one-shot classification systems capture semantic information within medical consultations. Results show that the embeddings are capable of capturing semantic features from text in a reliable and adaptable manner. Overall, Word2Vec, GloVe and Character n-grams embeddings performed well, indicating their suitability for modeling targeted to this task. GPT2 embedding also shows notable performance, indicating its suitability for models tailored to this task as well. Our machine learning architectures significantly improved the quality of health conversations when training data are scarce, improving communication between patients and healthcare providers.
DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation
University of Pittsburgh（匹兹堡大学）；Alibaba Group（阿里巴巴集团）；University of Waterloo（滑铁卢大学）；Shandong University（山东大学）；Skylenage；China University of Political Science and Law（中国政法大学）；Qwen Team, Alibaba Group（阿里巴巴集团Qwen团队）；Fordham University（福特汉姆大学）；Shanghai Jiao Tong University（上海交通大学）；Carnegie Mellon University（卡内基梅隆大学）；Tsinghua University（清华大学）
Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
Dr.Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian
National University of Science and Technology POLITEHNICA Bucharest（波兰技术科学大学）；Dalle Molle Institute for Artificial Intelligence Research (IDSIA)（达勒莫利人工智能研究所）；MedicChat
MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation
Sun Yat-sen University（中山大学）；Hong Kong University of Science and Technology（香港科技大学）；Hong Kong Polytechnic University（香港理工大学）；Tencent Jarvis Lab（腾讯天衍实验室）
AIPatient Arena: EHR-grounded evaluation of large language models in end-to-end clinical consultation workflows
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；Key Laboratory of Machine Intelligence and System Control, Shandong University（机器智能与系统控制重点实验室，山东大学）；Department of Medicine and Therapeutics, The Chinese University of Hong Kong（医学与治疗学系，香港中文大学）；Department of Geriatric Medicine, Qilu Hospital of Shandong University（老年医学科，山东大学齐鲁医院）；Department of Psychiatry, The Chinese University of Hong Kong（精神病学系，香港中文大学）；Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong（李秋虹家庭睡眠评估单元，精神病学系，医学院，香港中文大学）；Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong（李嘉诚健康科学研究院，医学院，香港中文大学）；Gerald Choa Neuroscience Institute, Department of Medicine and Therapeutics, The Chinese University of Hong Kong（Gerald Choa 神经科学研究所，医学与治疗学系，香港中文大学）
JMedEthicBench: A Multi-Turn Conversational Benchmark for Evaluating Medical Safety in Japanese Large Language Models
Kyoto University（京都大学）；Hohai University（河海大学）；The University of Tokyo（东京大学）；University of Science and Technology of China（中国科学技术大学）；Hong Kong Polytechnic University（香港理工大学）
A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations
School of Computer & Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；School of Computer Science, University of South China（南华大学计算机学院）；Faculty of Computer and Information Sciences, Hosei University（法政大学计算机与信息科学学院）
