@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://preview.aclanthology.org/ingest-ijcnlp-aacl/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."
}