MedDialog: Two Large-scale Medical Dialogue Datasets

2020/04/07 by Xuehai He, He, Xuehai, Shu Chen +23 · 14 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Speech and dialogue systems #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2004.03329

openalex publication_date 2020/04/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To facilitate the research and development of medical dialogue systems, we build two large-scale medical dialogue datasets: MedDialog-EN and MedDialog-CN. MedDialog-EN is an English dataset containing 0.3 million conversations between patients and doctors and 0.5 million utterances. MedDialog-CN is an Chinese dataset containing 1.1 million conversations and 4 million utterances. To our best knowledge, MedDialog-(EN,CN) are the largest medical dialogue datasets to date. The dataset is available at https://github.com/UCSD-AI4H/Medical-Dialogue-System

Cited by

• Fine-Tuning DialoGPT on Common Diseases in Rural Nepal for Medical Conversations
• MedRECT: A Medical Reasoning Benchmark for Error Correction in Clinical Texts
• CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation
• Plural Voices, Single Agent: Towards Inclusive AI in Multi-User Domestic Spaces
• Evaluating Medical LLMs by Levels of Autonomy: A Survey Moving from Benchmarks to Applications
• PrivacyPAD: A Reinforcement Learning Framework for Dynamic Privacy-Aware Delegation
• JEDA: Query-Free Clinical Order Search from Ambient Dialogues
• EduDial: Constructing a Large-scale Multi-turn Teacher-Student Dialogue Corpus
• Advances in Large Language Models for Medicine
• PG-CE: A Progressive Generation Dataset with Constraint Enhancement for Controllable Text Generation
• MORQA: Benchmarking Evaluation Metrics for Medical Open-Ended Question Answering
• End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning
• "Where does it hurt?" -- Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues
• Unveiling Super Experts in Mixture-of-Experts Large Language Models

Related

• Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges
• CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation
• MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs
• DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation
• MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations