SynthesisJournal of the European Academy of Dermatology and Venereology : JEADV2025
Diagnosis melanoma with artificial intelligence systems: A meta-analysis study and systematic review.
Synthesis in Journal of the European Academy of Dermatology and Venereology : JEADV, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis melanoma with artificial intelligence systems: A meta-analysis study and systematic review.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Pooled it
- Basic Principles of Skin Biopsy Optimization in Dermatopathology.Dermatopathology (Basel, Switzerland) · 2026Review
- Skin cancer detection and organ donation.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026Article
- Precision medicine in cutaneous melanoma-A comprehensive review.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026Review
- Artificial Intelligence and New Technologies in Melanoma Diagnosis: A Narrative Review.Cancers · 2025Review
- From Slide to Insight: The Emerging Alliance of Digital Pathology and AI in Melanoma Diagnostics.Cancers · 2025Review
- Advancing AI models for enhanced melanoma diagnosis.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Article
- Melanoma in Primary Care: A Narrative Review of Training Interventions and the Role of Telemedicine in Medical Education.Current oncology (Toronto, Ont.) · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundOne of the most promising and rapidly advancing research areas in recent years is using dermoscopic images for automatic diagnosis with artificial intelligence and machine learning methods.
objectivesThis study aimed to synthesize the existing studies for the clinical use of applications made with artificial intelligence methods and to summarize the predictive performance of deep learning and hybrid models-based algorithms in all these studies with a large-scale meta-analysis.
methodThe literature review was conducted between January 2006 and May 2024, and meta-analysis data were created by scanning the Web of Science (WOS), Scopus and MEDLINE databases. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist.
resultsA total of 2722 articles were evaluated. Data from 78 diagnostic tests from 39 primary studies meeting the inclusion and exclusion criteria were assessed. The pooled SROC overall model AUC was 0.96 [95% CI: 0.94-0.98], sensitivity was 0.89 [95% CI: 0.85-0.91] and specificity was 0.92 [95% CI: 0.90-0.94]. In the subgroup analyses, the pooled AUC was 0.98 [95% CI: 0.96-0.99] for HYBRID models.
conclusionsRecent studies have suggested that artificial intelligence algorithms and machine learning methods should be used extensively in medicine to assist physicians, especially in diagnosing melanoma. The ability of HYBRID model algorithms to predict diseases is promising. In particular, the performance of HYBRID models was found to be high. This information can assist clinicians in interpreting the most appropriate algorithms for diagnosing melanoma.
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Registered trials
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.