@inproceedings{fumi-etal-2026-reflector,
    title = "{R}eflect{OR}: an {LLM}-based Agent for Post-Operative Surgical Debriefing",
    author = "Fumi, Lorenzo  and
      Bombieri, Marco  and
      Allievi, Sara  and
      Bonvini, Stefano  and
      Chaspari, Theodora  and
      Zenati, Marco A.  and
      Giorgini, Paolo",
    editor = "Riccardi, Giuseppe  and
      Mousavi, Seyed Mahed  and
      Torres, Maria Ines  and
      Yoshino, Koichiro  and
      Callejas, Zoraida  and
      Chowdhury, Shammur Absar  and
      Chen, Yun-Nung  and
      Bechet, Frederic  and
      Gustafson, Joakim  and
      Damnati, G{\'e}raldine  and
      Papangelis, Alex  and
      D{'}Haro, Luis Fernando  and
      Mendon{\c{c}}a, John  and
      Bernardi, Raffaella  and
      Hakkani-Tur, Dilek  and
      Di Fabbrizio, Giuseppe {''}Pino{''}  and
      Kawahara, Tatsuya  and
      Alam, Firoj  and
      Tur, Gokhan  and
      Johnston, Michael",
    booktitle = "Proceedings of the 16th International Workshop on Spoken Dialogue System Technology",
    month = feb,
    year = "2026",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard-stats/2026.iwsds-1.40/",
    pages = "418--427",
    abstract = "Ineffective teamwork and communication can generate medical errors in the high-pressure environment of surgery, making post-operative debriefings essential for enhancing team performance and patient safety. However, these sessions are frequently rushed or incomplete due to clinicians' limited time. This paper introduces {R}eflect{OR}, an Agentic-{AI} architecture designed to support surgical debriefings by processing audio recordings from the operating room. The system employs specialized sub-agents that perform tasks such as generating summaries, constructing timelines of intraoperative events, identifying potential errors and counting the materials used. A qualitative evaluation indicates that the system effectively contextualizes transcripts, demonstrating its potential as a valuable tool for surgical debriefing. The paper also outlines key considerations for applying such an architecture in real-world clinical environments."
}