@inproceedings{shao-etal-2025-llm,
title = "{LLM}-Empowered Patient-Provider Communication: A Data-Centric Survey From a Clinical Perspective",
author = "Shao, Ruosi and
Seraj, Md Shamim and
Zhao, Kangyi and
Luo, Yingtao and
Li, Lincan and
Shen, Bolin and
Bates, Averi and
Zhao, Yue and
Pan, Chongle and
Hightow-Weidman, Lisa and
Chakraborty, Shayok and
Dong, Yushun",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.40/",
pages = "684--705",
ISBN = "979-8-89176-303-6",
abstract = "Large language models (LLMs) hold promise for advancing patient{--}provider communication, yet a persistent gap remains between benchmark-driven model development and the realities of clinical practice. This work presents a systematic, clinically grounded review of text-based medical datasets for LLM training and evaluation. We propose a scenario-based taxonomy derived from established clinical frameworks to map major knowledge-based and conversation-based corpora against core communication scenarios. We further synthesize core communication skills from gold-standard clinical assessment instruments and meta-analyze state-of-the-art medical LLM performance, highlighting how dataset properties, fine-tuning strategies, and evaluation metrics shape both knowledge acquisition and communicative competence. To empirically validate these findings, we conducted controlled fine-tuning experiments across representative LLMs, demonstrating that data composition and scenario alignment critically affect model performance. Our findings highlight the urgent need for scenario-rich datasets and standardized, human-centered evaluation protocol to advance clinically relevant medical LLMs."
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<abstract>Large language models (LLMs) hold promise for advancing patient–provider communication, yet a persistent gap remains between benchmark-driven model development and the realities of clinical practice. This work presents a systematic, clinically grounded review of text-based medical datasets for LLM training and evaluation. We propose a scenario-based taxonomy derived from established clinical frameworks to map major knowledge-based and conversation-based corpora against core communication scenarios. We further synthesize core communication skills from gold-standard clinical assessment instruments and meta-analyze state-of-the-art medical LLM performance, highlighting how dataset properties, fine-tuning strategies, and evaluation metrics shape both knowledge acquisition and communicative competence. To empirically validate these findings, we conducted controlled fine-tuning experiments across representative LLMs, demonstrating that data composition and scenario alignment critically affect model performance. Our findings highlight the urgent need for scenario-rich datasets and standardized, human-centered evaluation protocol to advance clinically relevant medical LLMs.</abstract>
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%0 Conference Proceedings
%T LLM-Empowered Patient-Provider Communication: A Data-Centric Survey From a Clinical Perspective
%A Shao, Ruosi
%A Seraj, Md Shamim
%A Zhao, Kangyi
%A Luo, Yingtao
%A Li, Lincan
%A Shen, Bolin
%A Bates, Averi
%A Zhao, Yue
%A Pan, Chongle
%A Hightow-Weidman, Lisa
%A Chakraborty, Shayok
%A Dong, Yushun
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F shao-etal-2025-llm
%X Large language models (LLMs) hold promise for advancing patient–provider communication, yet a persistent gap remains between benchmark-driven model development and the realities of clinical practice. This work presents a systematic, clinically grounded review of text-based medical datasets for LLM training and evaluation. We propose a scenario-based taxonomy derived from established clinical frameworks to map major knowledge-based and conversation-based corpora against core communication scenarios. We further synthesize core communication skills from gold-standard clinical assessment instruments and meta-analyze state-of-the-art medical LLM performance, highlighting how dataset properties, fine-tuning strategies, and evaluation metrics shape both knowledge acquisition and communicative competence. To empirically validate these findings, we conducted controlled fine-tuning experiments across representative LLMs, demonstrating that data composition and scenario alignment critically affect model performance. Our findings highlight the urgent need for scenario-rich datasets and standardized, human-centered evaluation protocol to advance clinically relevant medical LLMs.
%U https://aclanthology.org/2025.findings-ijcnlp.40/
%P 684-705
Markdown (Informal)
[LLM-Empowered Patient-Provider Communication: A Data-Centric Survey From a Clinical Perspective](https://aclanthology.org/2025.findings-ijcnlp.40/) (Shao et al., Findings 2025)
ACL
- Ruosi Shao, Md Shamim Seraj, Kangyi Zhao, Yingtao Luo, Lincan Li, Bolin Shen, Averi Bates, Yue Zhao, Chongle Pan, Lisa Hightow-Weidman, Shayok Chakraborty, and Yushun Dong. 2025. LLM-Empowered Patient-Provider Communication: A Data-Centric Survey From a Clinical Perspective. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 684–705, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.