TY - GEN
T1 - Medical Dialogue System
T2 - Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024
AU - Shi, Xiaoming
AU - Liu, Zeming
AU - Du, Li
AU - Wang, Yuxuan
AU - Wang, Hongru
AU - Guo, Yuhang
AU - Ruan, Tong
AU - Xu, Jie
AU - Zhang, Xiaofan
AU - Zhang, Shaoting
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - This paper surveys and organizes research works on 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, and 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 327 papers from well-known computer science, and natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshaped medical dialog systems' foundation. Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists the grand challenges of medical dialog systems, especially of large language models.
AB - This paper surveys and organizes research works on 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, and 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 327 papers from well-known computer science, and natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshaped medical dialog systems' foundation. Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists the grand challenges of medical dialog systems, especially of large language models.
UR - http://www.scopus.com/inward/record.url?scp=85205308657&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85205308657
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 2840
EP - 2861
BT - 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 - Proceedings of the Conference
A2 - Ku, Lun-Wei
A2 - Martins, Andre
A2 - Srikumar, Vivek
PB - Association for Computational Linguistics (ACL)
Y2 - 11 August 2024 through 16 August 2024
ER -