Evidence map›Paper›PMID 42168851›Full record

Observational studyBMC anesthesiology2026

Conversational artificial intelligence in the operating room: an exploratory cross-sectional single-center survey among anesthesiology residents.

Dario Gaetano, Viola Lomonaco, Salvatore Ferraioli, Vincenzo Pota, Umberto Colella, Maria Caterina Pace, Pasquale Sansone

Abstract readObservational Study
In one paragraph

Observational study in BMC anesthesiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Dario GaetanoDepartment of Women, Child and General and Specialized Surgery, University of Campania "Luigi Vanvitelli", Naples, 80138, Italy. dario.gaetano1@studenti.unicampania.it.
Viola LomonacoDepartment of Women, Child and General and Specialized Surgery, University of Campania "Luigi Vanvitelli", Naples, 80138, Italy.
Salvatore FerraioliDepartment of Women, Child and General and Specialized Surgery, University of Campania "Luigi Vanvitelli", Naples, 80138, Italy.
Vincenzo PotaDepartment of Women, Child and General and Specialized Surgery, University of Campania "Luigi Vanvitelli", Naples, 80138, Italy.
Umberto ColellaCritical Care Area, Anaesthesia and Postoperative Intensive Care Unit, AO Ospedale dei Colli, Monaldi Hospital, Naples, 80131, Italy.
Maria Caterina PaceDepartment of Women, Child and General and Specialized Surgery, University of Campania "Luigi Vanvitelli", Naples, 80138, Italy.
Pasquale SansoneDepartment of Women, Child and General and Specialized Surgery, University of Campania "Luigi Vanvitelli", Naples, 80138, Italy. pasquale.sansone@unicampania.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConversational artificial intelligence (AI) tools, such as large language models and chat-based systems, are increasingly used by clinicians and trainees. However, empirical evidence on how anesthesiology residents integrate conversational AI into perioperative workflows, especially within the operating room, is lacking. This exploratory single-center observational study aimed to characterize prevalence, patterns, and determinants of conversational AI use in the operating room.

methodsWe conducted a cross-sectional, anonymous online survey of anesthesiology and intensive care medicine residents in a single training program. The instrument was developed through iterative refinement and addressed knowledge, training, general AI usage habits, perioperative use, supervision, attitudes, safety perceptions, and data protection behaviors. Reporting followed STROBE and CHERRIES guidelines. The primary outcome was self-reported use of conversational AI in the operating room within the previous 3 months. Bivariate analyses and logistic regression examined associations with demographic, behavioral, and attitudinal factors.

resultsAmong 101 responses, 86 residents met inclusion criteria. The mean age was 31.8 (SD 3.9) years; 58.1% were female. Conversational AI use in the operating room was reported by 58.1% (95% CI 47.6-68.0%). Higher general frequency of AI use was independently associated with operating room use (adjusted OR 3.10; 95% CI 1.83-5.92). Training seniority was also associated with operating room use (adjusted OR 1.97 per additional residency year; 95% CI 1.27-3.33). Female gender was associated with higher odds of operating room use (adjusted OR 3.97; 95% CI 1.17-15.28). Attitudinal variables showed strong univariable associations with operating room conversational AI use, but these associations were attenuated after multivariable adjustment. Cronbach's alpha for the attitudinal scale was 0.79 (95% CI 0.71-0.85).

conclusionsIn this exploratory single-center study, conversational AI use in the operating room was reported by a substantial proportion of anesthesiology residents (~ 60%) and was associated with general AI usage patterns and residency seniority. Formal training, supervision, and institutional policies appeared limited. These findings highlight the need for competency-based educational initiatives and clear governance to support safe and effective integration of conversational AI into perioperative care.

Indexed as

AnesthesiologyArtificial IntelligenceInternship and ResidencyOperating RoomsAdultAttitude of Health PersonnelCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesAnesthesiology residentsClinical surveyConversational artificial intelligenceDigital healthMedical educationOperating roomPerioperative care

Identifiers

PMID42168851
PMCPMC13371682

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.