Evidence map›Paper›PMID 39613920›Full record

ArticleEmergency radiology2025

Diagnostic performance of ChatGPT in tibial plateau fracture in knee X-ray.

Mohammadreza Mohammadi, Sara Parviz, Parinaz Parvaz, Mohammad Mahdi Pirmoradi, Mohammad Afzalimoghaddam, Hadi Mirfazaelian

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Article in Emergency radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

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7citing papers in PubMed, 1 pooled it
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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.

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Mohammadreza MohammadiEmergency Medicine Department, Tehran University of Medical Sciences, Tehran, Iran.
Sara ParvizMusculoskeletal Imaging Research Center (MIRC), Tehran University of Medical Sciences, Tehran, Iran.
Parinaz ParvazRadiology Department, Tehran University of Medical Sciences, Tehran, Iran.
Mohammad Mahdi PirmoradiEmergency Medicine Department, Tehran University of Medical Sciences, Tehran, Iran.
Mohammad AfzalimoghaddamEmergency Medicine Department, Tehran University of Medical Sciences, Tehran, Iran.
Hadi MirfazaelianPrehospital and Hospital Emergency Research Center, Tehran University of Medical Sciences, Tehran, Iran. H-Mirfazaelian@sina.tums.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTibial plateau fractures are relatively common and require accurate diagnosis. Chat Generative Pre-Trained Transformer (ChatGPT) has emerged as a tool to improve medical diagnosis. This study aims to investigate the accuracy of this tool in diagnosing tibial plateau fractures.

methodsA secondary analysis was performed on 111 knee radiographs from emergency department patients, with 29 confirmed fractures by computed tomography (CT) imaging. The X-rays were reviewed by a board-certified emergency physician (EP) and radiologist and then analyzed by ChatGPT-4 and ChatGPT-4o. The diagnostic performances were compared using the area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, and likelihood ratios were also calculated.

resultsThe results indicated a sensitivity and negative likelihood ratio of 58.6% (95% CI: 38.9 - 76.4%) and 0.4 (95% CI: 0.3-0.7) for the EP, 72.4% (95% CI: 52.7 - 87.2%) and 0.3 (95% CI: 0.2-0.6) for the radiologist, 27.5% (95% CI: 12.7 - 47.2%) and 0.7 (95% CI: 0.6-0.9) for ChatGPT-4, and 55.1% (95% CI: 35.6 - 73.5%) and 0.4 (95% CI: 0.3-0.7) for ChatGPT4o. The specificity and positive likelihood ratio were 85.3% (95% CI: 75.8 - 92.2%) and 4.0 (95% CI: 2.1-7.3) for the EP, 76.8% (95% CI: 66.2 - 85.4%) and 3.1 (95% CI: 1.9-4.9) for the radiologist, 95.1% (95% CI: 87.9 - 98.6%) and 5.6 (95% CI: 1.8-17.3) for ChatGPT-4, and 93.9% (95% CI: 86.3 - 97.9%) and 9.0 (95% CI: 3.6-22.4) for ChatGPT4o. The area under the receiver operating characteristic curve (AUC) was 0.72 (95% CI: 0.6-0.8) for the EP, 0.75 (95% CI: 0.6-0.8) for the radiologist, 0.61 (95% CI: 0.4-0.7) for ChatGPT-4, and 0.74 (95% CI: 0.6-0.8) for ChatGPT4-o. The EP and radiologist significantly outperformed ChatGPT-4 (P value = 0.02 and 0.01, respectively), whereas there was no significant difference between the EP, ChatGPT-4o, and radiologist.

conclusionChatGPT-4o matched the physicians' performance and also had the highest specificity. Similar to the physicians, ChatGPT chatbots were not suitable for ruling out the fracture.

Indexed as

Tibial FracturesTomography, X-Ray ComputedAdultAgedAged, 80 and overEmergency Service, HospitalFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityTibial Plateau FracturesArtificial intelligenceChatGPTDiagnosisEmergency medicineRadiologyTibial plateau fracture

Identifiers

PMID39613920

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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.