@inproceedings{ben-abacha-etal-2023-overview,
    title = "Overview of the {MEDIQA}-Chat 2023 Shared Tasks on the Summarization {\&} Generation of Doctor-Patient Conversations",
    author = "Ben Abacha, Asma  and
      Yim, Wen-wai  and
      Adams, Griffin  and
      Snider, Neal  and
      Yetisgen, Meliha",
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.clinicalnlp-1.52",
    doi = "10.18653/v1/2023.clinicalnlp-1.52",
    pages = "503--513",
    abstract = "Automatic generation of clinical notes from doctor-patient conversations can play a key role in reducing daily doctors{'} workload and improving their interactions with the patients. MEDIQA-Chat 2023 aims to advance and promote research on effective solutions through shared tasks on the automatic summarization of doctor-patient conversations and on the generation of synthetic dialogues from clinical notes for data augmentation. Seventeen teams participated in the challenge and experimented with a broad range of approaches and models. In this paper, we describe the three MEDIQA-Chat 2023 tasks, the datasets, and the participants{'} results and methods. We hope that these shared tasks will lead to additional research efforts and insights on the automatic generation and evaluation of clinical notes.",
}

Automatic generation of clinical notes from doctor-patient conversations can play a key role in reducing daily doctors{'} workload and improving their interactions with the patients. MEDIQA-Chat 2023 aims to advance and promote research on effective solutions through shared tasks on the automatic summarization of doctor-patient conversations and on the generation of synthetic dialogues from clinical notes for data augmentation. Seventeen teams participated in the challenge and experimented with a broad range of approaches and models. In this paper, we describe the three MEDIQA-Chat 2023 tasks, the datasets, and the participants{'} results and methods. We hope that these shared tasks will lead to additional research efforts and insights on the automatic generation and evaluation of clinical notes.
Ben Abacha, Asma Yim, Wen-wai Adams, Griffin Snider, Neal Yetisgen, Meliha
Overview of the MEDIQA-Chat 2023 Shared Tasks on the Summarization & Generation of Doctor-Patient Conversations