Evidence map›Paper›PMID 40814205›Full record

ArticlePediatric dermatology

Digitizing Diagnoses: Distinguishing Infantile Hemangiomas From Other Vascular Anomalies.

Aretha On, Elena Huang, Jessica Hills, Jared Pasternak, Joanne Alfandre, Griffin Stockton Hogrogian, Albert C Yan

Abstract read
In one paragraph

Article in Pediatric dermatology. 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. Article
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

7 authors.

Aretha OnPerelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0009-0002-3221-5391
Elena HuangPerelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jessica HillsDivision of General Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Jared PasternakPrimary Care Network, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Joanne AlfandrePrimary Care Network, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Griffin Stockton HogrogianSection of Dermatology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Albert C YanPerelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-8403-8780

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesGenerative artificial intelligence (AI) models have become increasingly accessible and advanced with multimodal input. Infantile hemangiomas (IHs) are the most common pediatric vascular tumor, but pediatricians may have difficulty distinguishing them from similar-appearing lesions. We assess the capability of a public AI model, ChatGPT 4.0 (GPT) to distinguish between IHs and other vascular anomalies (VAs), evaluating its potential role as a clinical tool for pediatric clinicians.

methodsThis retrospective study assessed 50 IH and 50 non-IH VA images using a GPT zero-shot approach with the binary task of diagnosing an IH (or not). The same images were provided to four general pediatricians; comparison was performed between pediatricians and GPT.

resultsGPT achieves 75% accuracy in IH identification with an F1 score of 0.742. ROC curve generation yields an AUC of 0.80. Hundred-image analysis demonstrates that actual diagnosis affects GPT accuracy (p = 0.015). 50-IH-image analysis reveals configuration (p = 0.027), skin phototype (p = 0.019), and anatomical location (p = 0.047) as factors that may affect GPT accuracy. Comparing GPT to pediatricians reveals comparable results (p = 0.345).

conclusionThis off-the-shelf publicly available GPT (75%) was less accurate than a previously published well-trained AI that achieved higher accuracy (~92%). GPT's F1 score of 0.742 indicates moderate balance between precision and sensitivity, and an AUC calculation of 0.80 indicates a potential role for GPT as a clinical assistant in primary care settings. An untuned commercial GPT does not yet outperform pediatricians despite studies of well-trained AI. This study provides variables impacting GPT accuracy, serving as a foundation for future research.

Indexed as

Artificial IntelligenceHemangiomaSkin NeoplasmsVascular MalformationsDiagnosis, DifferentialFemaleHumansInfantMaleRetrospective StudiesROC Curveartificial intelligenceChatGPTinfantile hemangiomavascular anomaly

Identifiers

PMID40814205
PMCPMC12459423

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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.