Evidence map›Paper›PMID 41567299›Full record

ArticleComputational and structural biotechnology journal2026

ChatGPT-4V prompt: A tool to enhance junior radiologists' diagnostic capabilities in cystic renal masses to senior-level accuracy.

Dong-Dong Jin, Nan Zhang, Yan Wang, Ke Lin, Bin Qiao, Yue Yang, Jin-Hua Lin, Ding-Xiang Xie, Xiao-Yan Xie, Xiao-Hua Xie and 1 more

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 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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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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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

11 authors.

Dong-Dong JinDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, NO.58 Zhongshan Road 2, Guangzhou 510080, PR China.
Nan ZhangDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, NO.58 Zhongshan Road 2, Guangzhou 510080, PR China.
Yan WangDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, NO.58 Zhongshan Road 2, Guangzhou 510080, PR China.
Ke LinDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, NO.58 Zhongshan Road 2, Guangzhou 510080, PR China.
Bin QiaoDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, NO.58 Zhongshan Road 2, Guangzhou 510080, PR China.
Yue YangDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, NO.58 Zhongshan Road 2, Guangzhou 510080, PR China.
Jin-Hua LinDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, NO.58 Zhongshan Road 2, Guangzhou 510080, PR China.
Ding-Xiang XieDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Xiao-Yan XieDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, NO.58 Zhongshan Road 2, Guangzhou 510080, PR China.
Xiao-Hua XieDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, NO.58 Zhongshan Road 2, Guangzhou 510080, PR China.
Bo-Wen ZhuangDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, NO.58 Zhongshan Road 2, Guangzhou 510080, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to evaluate the diagnostic performance of prompt-adapted ChatGPT-4 Vision (ChatGPT-4V) in interpreting contrast-enhanced ultrasound (CEUS) images for the assessment of cystic renal masses (CRMs) using Bosniak classification. Additionally, it tested the ability of the best prompt to assist radiologists with different experience. Materials and methods: This retrospective study included 103 CRMs in patients who underwent CEUS and CT. ChatGPT-4V and six radiologists (three senior and three junior) independently assigned the Bosniak category (BC) based solely on CEUS images. Subsequently, radiologists re-assessed these images while reviewing ChatGPT-4V-generated BC and decided whether to modify their initial assessments. The diagnostic performance of radiologists and prompts was assessed using the area under the receiver operating characteristic curve (AUC). Results: The AUCs for prompts ranged from 0.507 to 0.688, whereas that for radiologists ranged from 0.685 to 0.831. Among all prompts, Reflection of Thoughts (ROT) prompt achieved the highest AUC, demonstrating performance comparable to juniors (0.688 vs. 0.715, P = 0.727). Although the AUC was lower than that of seniors (0.688 vs. 0.832, P = 0.019), ROT improved the AUCs of juniors: from 0.714 to 0.834 for junior 1, from 0.685 to 0.782 for junior 2, and from 0.704 to 0.783 for junior 3, with the post-assistance performance of all three being comparable to that of seniors. Conclusion: Prompt-adapted ChatGPT-4V showed variable performance in interpreting CEUS images. ROT as the best-performing prompt achieved diagnostic performance comparable to juniors, and it could help juniors achieve an AUC comparable to that of seniors.

Indexed as

Bosniak classificationChatGPT-4Vcontrasted enhanced ultrasoundCystic renal massesimage interpretationReflection of Thoughts prompt

Identifiers

PMID41567299
PMCPMC12818267

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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.