ReflectOR: an LLM-based Agent for Post-Operative Surgical Debriefing
Lorenzo Fumi, Marco Bombieri, Sara Allievi, Stefano Bonvini, Theodora Chaspari, Marco A. Zenati, Paolo Giorgini
Correct Metadata for
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 ReflectOR, 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.- Anthology ID:
- 2026.iwsds-1.40
- Volume:
- Proceedings of the 16th International Workshop on Spoken Dialogue System Technology
- Month:
- February
- Year:
- 2026
- Address:
- Trento, Italy
- Editors:
- Giuseppe Riccardi, Seyed Mahed Mousavi, Maria Ines Torres, Koichiro Yoshino, Zoraida Callejas, Shammur Absar Chowdhury, Yun-Nung Chen, Frederic Bechet, Joakim Gustafson, Géraldine Damnati, Alex Papangelis, Luis Fernando D’Haro, John Mendonça, Raffaella Bernardi, Dilek Hakkani-Tur, Giuseppe "Pino" Di Fabbrizio, Tatsuya Kawahara, Firoj Alam, Gokhan Tur, Michael Johnston
- Venue:
- IWSDS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 418–427
- Language:
- URL:
- https://aclanthology.org/2026.iwsds-1.40/
- DOI:
- Bibkey:
- Cite (ACL):
- Lorenzo Fumi, Marco Bombieri, Sara Allievi, Stefano Bonvini, Theodora Chaspari, Marco A. Zenati, and Paolo Giorgini. 2026. ReflectOR: an LLM-based Agent for Post-Operative Surgical Debriefing. In Proceedings of the 16th International Workshop on Spoken Dialogue System Technology, pages 418–427, Trento, Italy. Association for Computational Linguistics.
- Cite (Informal):
- ReflectOR: an LLM-based Agent for Post-Operative Surgical Debriefing (Fumi et al., IWSDS 2026)
- Copy Citation:
- PDF:
- https://aclanthology.org/2026.iwsds-1.40.pdf
Export citation
@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://aclanthology.org/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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="fumi-etal-2026-reflector">
<titleInfo>
<title>ReflectOR: an LLM-based Agent for Post-Operative Surgical Debriefing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lorenzo</namePart>
<namePart type="family">Fumi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marco</namePart>
<namePart type="family">Bombieri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Allievi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Stefano</namePart>
<namePart type="family">Bonvini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Theodora</namePart>
<namePart type="family">Chaspari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marco</namePart>
<namePart type="given">A</namePart>
<namePart type="family">Zenati</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Paolo</namePart>
<namePart type="family">Giorgini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-02</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 16th International Workshop on Spoken Dialogue System Technology</title>
</titleInfo>
<name type="personal">
<namePart type="given">Giuseppe</namePart>
<namePart type="family">Riccardi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Seyed</namePart>
<namePart type="given">Mahed</namePart>
<namePart type="family">Mousavi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="given">Ines</namePart>
<namePart type="family">Torres</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Koichiro</namePart>
<namePart type="family">Yoshino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zoraida</namePart>
<namePart type="family">Callejas</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shammur</namePart>
<namePart type="given">Absar</namePart>
<namePart type="family">Chowdhury</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yun-Nung</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Frederic</namePart>
<namePart type="family">Bechet</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joakim</namePart>
<namePart type="family">Gustafson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Géraldine</namePart>
<namePart type="family">Damnati</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alex</namePart>
<namePart type="family">Papangelis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Luis</namePart>
<namePart type="given">Fernando</namePart>
<namePart type="family">D’Haro</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">John</namePart>
<namePart type="family">Mendonça</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Raffaella</namePart>
<namePart type="family">Bernardi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dilek</namePart>
<namePart type="family">Hakkani-Tur</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giuseppe</namePart>
<namePart type="given">”Pino”</namePart>
<namePart type="family">Di Fabbrizio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tatsuya</namePart>
<namePart type="family">Kawahara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Firoj</namePart>
<namePart type="family">Alam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gokhan</namePart>
<namePart type="family">Tur</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michael</namePart>
<namePart type="family">Johnston</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Trento, Italy</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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 ReflectOR, 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.</abstract>
<identifier type="citekey">fumi-etal-2026-reflector</identifier>
<location>
<url>https://aclanthology.org/2026.iwsds-1.40/</url>
</location>
<part>
<date>2026-02</date>
<extent unit="page">
<start>418</start>
<end>427</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings %T ReflectOR: an LLM-based Agent for Post-Operative Surgical Debriefing %A Fumi, Lorenzo %A Bombieri, Marco %A Allievi, Sara %A Bonvini, Stefano %A Chaspari, Theodora %A Zenati, Marco A. %A Giorgini, Paolo %Y Riccardi, Giuseppe %Y Mousavi, Seyed Mahed %Y Torres, Maria Ines %Y Yoshino, Koichiro %Y Callejas, Zoraida %Y Chowdhury, Shammur Absar %Y Chen, Yun-Nung %Y Bechet, Frederic %Y Gustafson, Joakim %Y Damnati, Géraldine %Y Papangelis, Alex %Y D’Haro, Luis Fernando %Y Mendonça, John %Y Bernardi, Raffaella %Y Hakkani-Tur, Dilek %Y Di Fabbrizio, Giuseppe ”Pino” %Y Kawahara, Tatsuya %Y Alam, Firoj %Y Tur, Gokhan %Y Johnston, Michael %S Proceedings of the 16th International Workshop on Spoken Dialogue System Technology %D 2026 %8 February %I Association for Computational Linguistics %C Trento, Italy %F fumi-etal-2026-reflector %X 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 ReflectOR, 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. %U https://aclanthology.org/2026.iwsds-1.40/ %P 418-427
Markdown (Informal)
[ReflectOR: an LLM-based Agent for Post-Operative Surgical Debriefing](https://aclanthology.org/2026.iwsds-1.40/) (Fumi et al., IWSDS 2026)
- ReflectOR: an LLM-based Agent for Post-Operative Surgical Debriefing (Fumi et al., IWSDS 2026)
ACL
- Lorenzo Fumi, Marco Bombieri, Sara Allievi, Stefano Bonvini, Theodora Chaspari, Marco A. Zenati, and Paolo Giorgini. 2026. ReflectOR: an LLM-based Agent for Post-Operative Surgical Debriefing. In Proceedings of the 16th International Workshop on Spoken Dialogue System Technology, pages 418–427, Trento, Italy. Association for Computational Linguistics.