Evidence map›Paper›PMID 40398897›Full record

ArticleBMJ health & care informatics2025

Artificial intelligence in clinical practice: a cross-sectional survey of paediatric surgery residents' perspectives.

Francesca Gigola, Tommaso Amato, Marco Del Riccio, Alessandro Raffaele, Antonino Morabito, Riccardo Coletta

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

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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

6 authors.

Francesca Gigola *School of Pediatric Surgery, University of Florence, Florence, Italy francesca.gigola@unifi.it.ORCID http://orcid.org/0000-0001-6732-4243
Tommaso Amato *School of Pediatric Surgery, University of Florence, Florence, Italy.
Marco Del RiccioDepartment of Health Sciences, University of Florence, Florence, Italy.
Alessandro RaffaeleDepartment of Maternal and Child Health, IRCCS Policlinico San Matteo Pavia, Pavia, Italy.
Antonino MorabitoDepartment of Neurosciences Psychology, University of Florence, Florence, Italy.
Riccardo ColettaDepartment of Neurosciences Psychology, University of Florence, Florence, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe aim of this study was to compare the performances of residents and ChatGPT in answering validated questions and assess paediatric surgery residents' acceptance, perceptions and readiness to integrate artificial intelligence (AI) into clinical practice.

methodsWe conducted a cross-sectional study using randomly selected questions and clinical cases on paediatric surgery topics. We examined residents' acceptance of AI before and after comparing their results to ChatGPT's results using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Data analysis was performed using Jamovi V.2.4.12.0.

results30 residents participated. ChatGPT-4.0's median score was 13.75, while ChatGPT-3.5's was 8.75. The median score among residents was 8.13. Differences appeared statistically significant. ChatGPT outperformed residents specifically in definition questions (ChatGPT-4.0 vs residents, p<0.0001; ChatGPT-3.5 vs residents, p=0.03). In the UTAUT2 Questionnaire, respondents expressed a more positive evaluation of ChatGPT with higher mean values for each construct and lower fear of technology after learning about test scores. DISCUSSION: ChatGPT performed better than residents in knowledge-based questions and simple clinical cases. The accuracy of ChatGPT declined when confronted with more complex questions. The UTAUT questionnaire results showed that learning about the potential of ChatGPT could lead to a shift in perception, resulting in a more positive attitude towards AI.

conclusionOur study reveals residents' positive receptivity towards AI, especially after being confronted with its efficacy. These results highlight the importance of integrating AI-related topics into medical curricula and residency to help future physicians and surgeons better understand the advantages and limitations of AI.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelInternship and ResidencyPediatricsAdultCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesArtificial intelligenceDeep LearningInformaticsMachine Learning

Identifiers

PMID40398897
PMCPMC12097045

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.