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; Lotanna Nwandu; Robert Biossonnette; Justin M Ko; Eleni Linos

doi:10.1093/bjd/ljaf250

Assessing the Performance of Artificial Intelligence Models in Evaluating Inflammatory Skin Disease Severity: A Systematic Review and Meta-Analysis

Br J Dermatol. 2025 Jun 26:ljaf250. doi: 10.1093/bjd/ljaf250. Online ahead of print.

Authors

Zhuo Ran Cai^{1

2

3}, Jiyeong Kim^{1

2}, Shawheen J Rezaei^{1

2}, Michael L Chen^{1

2}, Fadi Touma⁴, Catherine Zhu⁴, Sonia Onyeka^{1

2}, Fonette Fonjungo⁵, Jesutofunmi A Omiye^{1

2

6}, Isabelle Krakowski^{1

2

7}, Lotanna Nwandu⁸, Robert Biossonnette⁹, Justin M Ko¹, Eleni Linos^{1

2}

Affiliations

¹ Department of Dermatology, School of Medicine, Stanford University, Stanford, CA, USA.
² Center for Digital Health, School of Medicine, Stanford University, Stanford, CA, USA.
³ Department of Dermatology, School of Medicine, University of Montreal, Montreal, Quebec, Canada.
⁴ Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada.
⁵ Meharry Medical College, Nashiville, TN USA.
⁶ Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
⁷ Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.
⁸ Carle Illinois College of Medicine, Champaign-Urbana, IL, USA.
⁹ Innovaderm Research, Montreal, Quebec, Canada.

PMID: 40570030
DOI: 10.1093/bjd/ljaf250

Abstract

Background and objectives: Artificial intelligence (AI) applications in dermatology have expanded beyond diagnosis and have shifted towards assessing disease severity. We aim to qualitatively and quantitatively evaluate the performance of image-based AI models in severity assessment for various skin diseases.

Methods: In this systematic review and meta-analysis, we collected studies using four electronic databases, including PubMed, Embase, IEEE Xplore, and Web of Science, published from January 1, 2017, to April 6, 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 non-validated 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 re-constructed 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.

Results: Our 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% 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 (EASI 97.3% vs IGA for atopic dermatitis 78.9%, p<0.0001; Hayashi Grading 89.7% vs IGA for acne 69.8%, p<0.0001).

Conclusions: Our 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.

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