Papers with medical dialogue systems
MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs (2022.emnlp-demos)
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| Challenge: | Existing medical dialogue systems have the problems of weak scalability, insufficient knowledge, and poor controllability. |
| Approach: | They propose a medical conversational question-answering system based on the knowledge graph to improve scalability and controllability. |
| Outcome: | The proposed system can conduct knowledge-grounded dialogues with users, using a Chinese medical knowledge graph and a large-scale dataset. |
Benchmarking Large Language Models on Communicative Medical Coaching: A Dataset and a Novel System (2024.findings-acl)
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| Challenge: | Existing applications of natural language processing (NLP) focus on patient-centered services, but the potential of NLP to benefit inexperienced doctors remains unexplored. |
| Approach: | They propose a human-AI cooperative framework to assist medical learners in practicing communication skills during patient consultations. |
| Outcome: | The proposed framework enables medical learners to practice communication skills during patient consultations while a coach agent provides immediate, structured feedback. |
Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges (2024.findings-acl)
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Xiaoming Shi, Zeming Liu, Li Du, Yuxuan Wang, Hongru Wang, Yuhang Guo, Tong Ruan, Jie Xu, Xiaofan Zhang, Shaoting Zhang
| Challenge: | Existing medical dialogue systems have significant potential to simplify diagnostic procedure and reduce the cost of collecting information from patients. |
| Approach: | They analyze 325 papers from well-known computer science, natural language processing conferences and journals to find out the major challenges of medical dialog systems. |
| Outcome: | The proposed systems have been surveyed in the medical community but have not been evaluated from a technical perspective. |
MidMed: Towards Mixed-Type Dialogues for Medical Consultation (2023.acl-long)
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| Challenge: | Current medical dialogue systems assume that patients have explicit goals but are often unavailable in real-world situations due to the lack of medical knowledge. |
| Approach: | They propose a human-to-human mixed-type medical consultation dialogue corpus . they build benchmarking baselines on MidMed and propose an instruction-guiding framework . Experimental results show the effectiveness of InsMed . |
| Outcome: | The proposed system can help patients clarify their goals in real-world situations . it covers four departments with 8,309 dialogues and provides benchmarking baselines . |
MedDialog: Large-scale Medical Dialogue Datasets (2020.emnlp-main)
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Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
| Challenge: | telemedicine is a medical practice that provides patient care remotely using video conferencing tools. |
| Approach: | They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance . |
| Outcome: | The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues. |