The Use of Ambient Dictation Artificial Intelligence in Clinical Spaces in Surgery: A Scoping Review
- 1Department of Otolaryngology–Head and Neck Surgery Icahn School of Medicine at Mount Sinai New York New York USA
Abstract
Objective To determine the current scope of knowledge regarding ambient listening artificial intelligence (AI) tools for documentation in surgical clinical environments. Data Sources Literature search was performed using EMBASE, PubMed (MEDLINE), CINAHL, SCOPUS, and Cochrane databases. Citation searching was utilized to identify additional studies. Methods A scoping review regarding the use of ambient dictation artificial intelligence in clinical note generation in surgery was performed per PRISMA‐ScR guidelines. Two reviewers independently performed title and abstract screening, full‐text review, and data extraction. Inclusion criteria included studies describing the use of ambient listening AI tools for the generation of clinical documentation for surgery, with full‐text available, in English or English‐language translation. Results Of 252 studies identified through database and citation searching, 12 met the inclusion criteria. Narrative reviews were most common (n = 8), followed by scoping reviews (n = 1), systematic reviews (n = 1), primary research (n = 1), and expert commentary (n = 1). Three studies provided original data: the primary research and expert commentary focused on the use of AI scribing technology in urology, and one narrative review also included survey data regarding the implementation of AI scribing in hand surgery. All three sources found positive clinician feedback following the implementation of AI scribing technology. All review articles meeting inclusion criteria expressed positive views regarding the implementation of AI scribing technology in surgical subspecialties, though most relied on sources describing AI scribing in nonsurgical subspecialties. Conclusion Documentation burden is a common contributor to clinician burnout in the medical field. AI scribing technologies have the potential to promote clinic efficiency and decrease documentation burden. The current research landscape regarding the implementation of these tools in surgical specialties is sparse and warrants further primary research in the future.
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BibTeX
@article{weiss2026ambient,
title = {{The Use of Ambient Dictation Artificial Intelligence in Clinical Spaces in Surgery: A Scoping Review}},
author = {Orli Weiss and Alyssa Steinbaum and Carly Fiest and Lacy Brame and Alfred Marc Iloreta},
journal = {World Journal of Otorhinolaryngology-Head and Neck Surgery},
year = {2026},
month = oct,
doi = {10.1002/wjo2.70135},
url = {https://doi.org/10.1002/wjo2.70135},
note = {CC BY},
}
RIS
TY - JOUR
TI - The Use of Ambient Dictation Artificial Intelligence in Clinical Spaces in Surgery: A Scoping Review
AU - Orli Weiss
AU - Alyssa Steinbaum
AU - Carly Fiest
AU - Lacy Brame
AU - Alfred Marc Iloreta
PY - 2026
DA - 2026/10/23
JO - World Journal of Otorhinolaryngology-Head and Neck Surgery
DO - 10.1002/wjo2.70135
UR - https://doi.org/10.1002/wjo2.70135
AB - Objective To determine the current scope of knowledge regarding ambient listening artificial intelligence (AI) tools for documentation in surgical clinical environments. Data Sources Literature search was performed using EMBASE, PubMed (MEDLINE), CINAHL, SCOPUS, and Cochrane databases. Citation searching was utilized to identify additional studies. Methods A scoping review regarding the use of ambient dictation artificial intelligence in clinical note generation in surgery was performed per PRISMA‐ScR guidelines. Two reviewers independently performed title and abstract screening, full‐text review, and data extraction. Inclusion criteria included studies describing the use of ambient listening AI tools for the generation of clinical documentation for surgery, with full‐text available, in English or English‐language translation. Results Of 252 studies identified through database and citation searching, 12 met the inclusion criteria. Narrative reviews were most common (n = 8), followed by scoping reviews (n = 1), systematic reviews (n = 1), primary research (n = 1), and expert commentary (n = 1). Three studies provided original data: the primary research and expert commentary focused on the use of AI scribing technology in urology, and one narrative review also included survey data regarding the implementation of AI scribing in hand surgery. All three sources found positive clinician feedback following the implementation of AI scribing technology. All review articles meeting inclusion criteria expressed positive views regarding the implementation of AI scribing technology in surgical subspecialties, though most relied on sources describing AI scribing in nonsurgical subspecialties. Conclusion Documentation burden is a common contributor to clinician burnout in the medical field. AI scribing technologies have the potential to promote clinic efficiency and decrease documentation burden. The current research landscape regarding the implementation of these tools in surgical specialties is sparse and warrants further primary research in the future.
SN - 2589-1081
SN - 2095-8811
ER -