@inproceedings{shi-etal-2024-medical,
title = "Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges",
author = "Shi, Xiaoming and
Liu, Zeming and
Du, Li and
Wang, Yuxuan and
Wang, Hongru and
Guo, Yuhang and
Ruan, Tong and
Xu, Jie and
Zhang, Xiaofan and
Zhang, Shaoting",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.167",
doi = "10.18653/v1/2024.findings-acl.167",
pages = "2840--2861",
abstract = "This paper surveys and organizes research works of medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshape medical dialog systems{'} foundation.Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists grand challenges of medical dialog systems, especially of large language models.",
}
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<abstract>This paper surveys and organizes research works of medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshape medical dialog systems’ foundation.Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists grand challenges of medical dialog systems, especially of large language models.</abstract>
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%0 Conference Proceedings
%T Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges
%A Shi, Xiaoming
%A Liu, Zeming
%A Du, Li
%A Wang, Yuxuan
%A Wang, Hongru
%A Guo, Yuhang
%A Ruan, Tong
%A Xu, Jie
%A Zhang, Xiaofan
%A Zhang, Shaoting
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F shi-etal-2024-medical
%X This paper surveys and organizes research works of medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshape medical dialog systems’ foundation.Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists grand challenges of medical dialog systems, especially of large language models.
%R 10.18653/v1/2024.findings-acl.167
%U https://aclanthology.org/2024.findings-acl.167
%U https://doi.org/10.18653/v1/2024.findings-acl.167
%P 2840-2861
Markdown (Informal)
[Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges](https://aclanthology.org/2024.findings-acl.167) (Shi et al., Findings 2024)
ACL
- Xiaoming Shi, Zeming Liu, Li Du, Yuxuan Wang, Hongru Wang, Yuhang Guo, Tong Ruan, Jie Xu, Xiaofan Zhang, and Shaoting Zhang. 2024. Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges. In Findings of the Association for Computational Linguistics ACL 2024, pages 2840–2861, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.