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

Objectives
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, 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).

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% 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].

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.