Evidence map›Paper›PMID 40818131›Full record

ArticleMedicina oral, patologia oral y cirugia bucal2025

Artificial intelligence in maxillofacial trauma: expert ally or unreliable assistant?

N Agbulut, M Unlu

Abstract read
In one paragraph

Article in Medicina oral, patologia oral y cirugia bucal, 2025. 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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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

2 authors.

N AgbulutIstanbul Medipol University, School of Dentistry Department of Oral and Maxillofacial Surgery Istanbul, Turkey nelliagbulut@gmail.com.
M Unlu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLarge language models (LLMs), such as ChatGPT, have demonstrated potential in synthesizing complex clinical information, yet concerns persist regarding their accuracy and reliability in specialized domains. The rationale of this study is to address a gap in the literature by evaluating ChatGPT-4o's capabilities and limitations in terms of accuracy and reliability on oral and maxillofacial traumatology. MATERIAL AND

methodsA total of 188 oral and maxillofacial trauma-related questions were selected from a comprehensive resource. Thirty questions were randomly chosen and submitted to ChatGPT-4o resetting to "new chat" mode every repetition to eliminate potential memory bias. Accuracy was scored using a 3-point Likert scale. Reliability was assessed with weighted kappa (κ) and Intraclass Correlation Coefficient (ICC), and internal consistency was evaluated using both Cronbach's alpha (α) and McDonald's omega (ω).

resultsThe accuracy rates for comprehensive and adequate responses were calculated as 38% (95% CI: 32.5% - 43.5%) and 58% (95% CI: 52.1% - 63.3%), respectively. Weighted kappa (κ = 0.469) and ICC (0.503) indicated moderate reliability. Internal consistency metrics revealed excellent and good reliability, respectively (α = 0.904, ω = 0.860).

conclusionsChatGPT-4o demonstrated promising results as an adjunct tool in providing supplementary educational content, verifying critical information, and supporting the decision-making processes in oral and maxillofacial traumatology. Current limitations warrant further research. Future enhancements in LLMs and prompt engineering may assist in the optimization of their clinical applicability and alignment with evidence-based standards.

Indexed as

Artificial IntelligenceMaxillofacial InjuriesHumansReproducibility of Results

Identifiers

PMID40818131
PMCPMC12395565

What OpenQuestion holds

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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.