The AI Doctor Is In: A Survey of Task-Oriented Dialogue Systems for Healthcare Applications

Mina Valizadeh, Natalie Parde


Abstract
Task-oriented dialogue systems are increasingly prevalent in healthcare settings, and have been characterized by a diverse range of architectures and objectives. Although these systems have been surveyed in the medical community from a non-technical perspective, a systematic review from a rigorous computational perspective has to date remained noticeably absent. As a result, many important implementation details of healthcare-oriented dialogue systems remain limited or underspecified, slowing the pace of innovation in this area. To fill this gap, we investigated an initial pool of 4070 papers from well-known computer science, natural language processing, and artificial intelligence venues, identifying 70 papers discussing the system-level implementation of task-oriented dialogue systems for healthcare applications. We conducted a comprehensive technical review of these papers, and present our key findings including identified gaps and corresponding recommendations.
Anthology ID:
2022.acl-long.458
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6638–6660
Language:
URL:
https://aclanthology.org/2022.acl-long.458
DOI:
10.18653/v1/2022.acl-long.458
Bibkey:
Cite (ACL):
Mina Valizadeh and Natalie Parde. 2022. The AI Doctor Is In: A Survey of Task-Oriented Dialogue Systems for Healthcare Applications. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6638–6660, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
The AI Doctor Is In: A Survey of Task-Oriented Dialogue Systems for Healthcare Applications (Valizadeh & Parde, ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.458.pdf
Video:
 https://aclanthology.org/2022.acl-long.458.mp4