Evidence map›Paper›PMID 40570030›Full record

SynthesisThe British journal of dermatology2025

Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis.

Zhuo Ran Cai, Jiyeong Kim, Shawheen J Rezaei, Michael L Chen, Fadi Touma, Catherine Zhu, Sonia Onyeka, Fonette Fonjungo, Jesutofunmi A Omiye, Isabelle Krakowski and 4 more

Abstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in The British journal of dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

14 authors.

Zhuo Ran CaiDepartment of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA.
Jiyeong KimDepartment of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA.
Shawheen J RezaeiDepartment of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA.ORCID 0000-0002-4394-7371
Michael L ChenDepartment of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA.ORCID 0000-0002-0168-1122
Fadi ToumaFaculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.ORCID 0000-0002-1338-7289
Catherine ZhuFaculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.
Sonia OnyekaDepartment of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA.ORCID 0009-0006-5757-1180
Fonette FonjungoMeharry Medical College, Nashville, TN, USA.
Jesutofunmi A OmiyeDepartment of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA.
Isabelle KrakowskiDepartment of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA.
Lotanna NwanduCarle Illinois College of Medicine, Champaign-Urbana, IL, USA.
Robert BiossonnetteInnovaderm Research, Montreal, QC, Canada.
Justin M KoDepartment of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA.
Eleni LinosDepartment of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA.

Funding

Patient Oriented Research in Vulnerable Populations with Skin DiseaseK24AR075060 · NIAMS · STANFORD UNIVERSITY · PI Eleni Linos · 2019 to 2026
$1.6M
Leveraging Large Language Models and Machine Learning Algorithms to Assess Depression and Anxiety Symptoms and Risks for Patients with Cardiovascular Disease or Diabetes MellitusK01MH137386 · NIMH · STANFORD UNIVERSITY · PI Jiyeong Kim · 2024 to 2026
$406k
Midcareer Investigator Award in Patient-Oriented Research K24AR075060NIAMS NIH HHS K24 AR075060NIH HHS K01MH137386NIMH NIH HHS K01 MH137386
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) applications in dermatology have expanded beyond diagnosis and have shifted towards assessing disease severity.

objectivesTo qualitatively and quantitatively evaluate the performance of image-based AI models in severity assessment for various skin diseases.

methodsIn this systematic review and meta-analysis, we collected studies using four electronic databases, including PubMed, Embase, Institute of Electrical and Electronics Engineers Xplore and Web of Science, published from 1 January 2017 to 6 April 2023, and updated the search in November 2023. Studies assessing the performance of deep learning AI models on the severity of skin diseases were included. We excluded studies that utilized a nonvalidated severity index, lacked clinical images, and assessed wounds, ulcers or burns. Two independent reviewers extracted prespecified study characteristics for the summary table. For the meta-analysis, contingency tables were extracted, when possible, and reconstructed for each severity measure. Accuracy was calculated using a bivariate model in Metandi package, and meta-regression was performed by disease type and scoring system. This study was registered with PROSPERO (CRD42023487228).

resultsOur initial search identified 7737 records. After duplicate removal and abstract screening, we reviewed the full text of 192 articles and included 45 studies for systematic review and 19 for meta-analysis. The pooled sensitivity and specificity of AI models were 80.5% [95% confidence interval (CI) 76.2-84.2] and 96.2% (95% CI 94.9-97.2), respectively. Moreover, pooled sensitivity differed by disease (atopic dermatitis 91.8% vs. acne 80.7%, P = 0.005; acne 80.4% vs. psoriasis 71.1%, P = 0.044) and scoring system [Eczema Area and Severity Index 97.3% vs. Investigator's Global Assessment (IGA) for atopic dermatitis 78.9%, P < 0.001; Hayashi Grading 89.7% vs. IGA for acne 69.8%, P < 0.001].

conclusionsOur findings show that current AI models exhibit a high level of capacity in disease severity assessment. Nevertheless, efforts are urgently needed to improve transparency in data reporting and conduct high-quality prospective studies using objective reference standards in clinical settings to generate reliable evidence.

Indexed as

Artificial IntelligenceDeep LearningDermatologyAcne VulgarisHumansSeverity of Illness Index

Identifiers

PMID40570030
PMCPMC12532143

What OpenQuestion holds

Textmetadata
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.