Evidence map›Paper›PMID 40116774›Full record

ArticleJMIR dermatology2025

Assessing the Diagnostic Accuracy of ChatGPT-4 in Identifying Diverse Skin Lesions Against Squamous and Basal Cell Carcinoma.

Nitin Chetla, Matthew Chen, Joseph Chang, Aaron Smith, Tamer Rajai Hage, Romil Patel, Alana Gardner, Bridget Bryer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Nitin Chetla *University of Virginia School of Medicine, University of Virginia, 828 Cabell Avenue, Charlottesville, VA, 22903, United States, 1 571-581-0562.ORCID 0009-0003-3496-262X
Matthew Chen *Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States.ORCID 0009-0000-4950-3686
Joseph ChangUniversity of Passau, Passau, Germany.ORCID 0009-0004-3152-4728
Aaron SmithUniversity of Virginia School of Medicine, University of Virginia, 828 Cabell Avenue, Charlottesville, VA, 22903, United States, 1 571-581-0562.ORCID 0009-0002-1029-9125
Tamer Rajai HageVirginia Tech, Blacksburg, VA, United States.ORCID 0009-0002-6407-954X
Romil PatelUniversity of Virginia School of Medicine, University of Virginia, 828 Cabell Avenue, Charlottesville, VA, 22903, United States, 1 571-581-0562.ORCID 0009-0009-4536-3802
Alana GardnerUniversity at Albany, State University of New York, Albany, NY, United States.ORCID 0009-0005-1144-439X
Bridget BryerDepartment of Dermatology, University of Virginia, Charlottesville, VA, United States.ORCID 0009-0002-7487-5822

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Our study evaluates the diagnostic accuracy of ChatGPT-4o in classifying various skin lesions, highlighting its limitations in distinguishing squamous cell carcinoma from basal cell carcinoma using dermatoscopic images.

Indexed as

Basal Cell CarcinomaCarcinoma, Squamous CellDermoscopySkin NeoplasmsAgedDiagnosis, DifferentialFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedAIAI in dermatologyalgorithmanalyticsartificial intelligencebasal cell carcinomachatbotChatGPTChatGPT-4dermatologistdermatoscopic image analysisdiagnostic accuracymachine learningMLmodelskin cancerskin cancer detectionskin lesion differentiationsquamous cell carcinoma

Identifiers

PMID40116774
PMCPMC11951809

What OpenQuestion holds

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LicenceCC BY
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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.