@article{zhoujian2026,
title = {Towards Fluid Consultations: Design and Real-World Evaluation of a Multi-Agent, Full-Duplex Health AI Assistant},
author = {Sun, Zhoujian and Yang, Minghui and Wang, Jian and Gu, Jinjie and Liu, Junwei},
journal = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track},
year = {2026},
month = {Jan},
doi = {10.18653/2026.emnlp-industry.36},
url = {https://doi.org/10.18653/2026.emnlp-industry.36},
}
Sun, Zhoujian et al. (2026). Towards Fluid Consultations: Design and Real-World Evaluation of a Multi-Agent, Full-Duplex Health AI Assistant. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track. https://doi.org/10.18653/2026.emnlp-industry.36
Sun, Zhoujian, et al.. "Towards Fluid Consultations: Design and Real-World Evaluation of a Multi-Agent, Full-Duplex Health AI Assistant." Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, 2026, https://doi.org/10.18653/2026.emnlp-industry.36.
Sun, Zhoujian, et al.. "Towards Fluid Consultations: Design and Real-World Evaluation of a Multi-Agent, Full-Duplex Health AI Assistant." Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track (2026). https://doi.org/10.18653/2026.emnlp-industry.36
Sun, Zhoujian et al., 2026. Towards Fluid Consultations: Design and Real-World Evaluation of a Multi-Agent, Full-Duplex Health AI Assistant. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track. Available at: <https://doi.org/10.18653/2026.emnlp-industry.36>.
TY - JOUR
TI - Towards Fluid Consultations: Design and Real-World Evaluation of a Multi-Agent, Full-Duplex Health AI Assistant
AU - Sun, Zhoujian
AU - Yang, Minghui
AU - Wang, Jian
AU - Gu, Jinjie
AU - Liu, Junwei
PY - 2026
JO - Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
DO - 10.18653/2026.emnlp-industry.36
UR - https://doi.org/10.18653/2026.emnlp-industry.36
ER -