In a systematic review and meta-analysis published in JAMA Dermatology, Laiouar-Pedari et al evaluated the real-world diagnostic performance of artificial intelligence (AI)–assisted dermoscopy for melanoma detection. The study was undertaken to address a critical gap in the literature: while prior retrospective studies have suggested that AI can match or exceed dermatologist performance, prospective evidence—more reflective of clinical practice—has been limited. By focusing exclusively on prospective studies, the authors sought to determine whether AI is ready for routine clinical use and whether it can meaningfully augment clinician performance in melanoma diagnosis.
Study Details
The analysis included 11 prospective studies comprising more than 2,500 patients and over 50 dermatologists. Eligible studies evaluated adult patients with suspected melanoma using dermoscopic images, with histopathology as the reference standard. The investigators compared three diagnostic approaches: dermatologists alone, AI alone (primarily convolutional neural network–based systems), and dermatologists assisted by AI.
Data were systematically extracted and pooled for sensitivity, specificity, accuracy, and balanced accuracy. Studies were required to include at least 20 histopathologically confirmed melanoma cases and to use prospective designs, excluding retrospective datasets and nondermoscopic imaging modalities. Risk of bias was assessed using QUADAS-2 and QUADAS-C tools, revealing frequent concerns related to patient selection and study design, particularly the preselection of lesions suspicious for melanoma and the use of simplified binary classification systems.
Key Results
Across studies, dermatologists achieved a pooled sensitivity of 78.6% (95% confidence interval [CI] = 67.5%–88.1%) and specificity of 75.2% (95% CI = 63.3%–84.3%). AI systems demonstrated comparable performance, with sensitivity of 80.9% (95% CI = 63.6%–94.5%) and specificity of 75.6% (95% CI = 64.5%–85.6%). In the single study evaluating AI-assisted dermatologists, performance improved further, with sensitivity of 91.9% and specificity of 83.7%. Head-to-head comparisons suggested that AI may offer higher specificity with similar sensitivity, potentially reducing unnecessary biopsies. However, variability across studies was substantial, and many designs introduced bias—most notably through restricted patient populations and binary diagnostic frameworks that do not reflect real-world clinical complexity.
According to the study authors, the findings indicate that AI can achieve dermatologist-level diagnostic accuracy in prospective settings and may enhance performance when integrated into clinical workflows, but current evidence remains preliminary.
“In the systematic review and meta-analysis of prospective settings, AI systems perform at comparable levels to dermatologists for melanoma diagnostics and may enhance performance when used as a decision-support tool. However, the frequent risk of bias and limited generalizability of current studies highlight the need for broader validation in unselected patient populations in the clinical setting,” they concluded.
Sara Laiouar-Pedari, PhD, of German Cancer Research Center, Heidelberg, Germany, is the corresponding author for the JAMA Dermatology article.
DISCLOSURE: The study was funded by the Ministry of Health, Social Affairs and Integration Baden-Württemberg, Stuttgart, Germany. For full disclosures of the study authors, visit jamanetwork.com.
