ReviewNPJ digital medicine2024
A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT07506967 (Early Detection and Management of SKIN-related negleCted Tropical Diseases Using Artificial Intelligence in Sub-saharan afRica), which is not on this map. Cited by 60 papers, 6 of them syntheses 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.
Early Detection and Management of SKIN-related negleCted Tropical Diseases Using Artificial Intelligence in Sub-saharan afRica (SkincAIr)
Who cites it
60 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Evaluating the Performance of Artificial Intelligence in Accurately Detecting Skin Cancer: An Umbrella Review of Systematic Reviews and Meta-analyses.American journal of clinical dermatology · 2026Pooled it
- Prospective Evidence on Artificial Intelligence-Assisted Melanoma Diagnostics: A Systematic Review and Meta-Analysis.JAMA dermatology · 2026Pooled it
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Deep Learning Algorithms in the Diagnosis of Basal Cell Carcinoma Using Dermatoscopy: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Diagnostic Accuracy of Novel Optical Imaging Techniques for Melanoma Detection: A Systematic Review and Meta-Analysis.International journal of dermatology · 2025Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- The benchmark illusion: on the curious underestimation of AI and the uncertainty of its creators' gold standards.EClinicalMedicine · 2026Review
- The Evolving Role of Artificial Intelligence in Dermatology: A Meta-Analysis of Diagnostic Performance, Clinical Applications, and Implementation Challenges (2003-2025).Diagnostics (Basel, Switzerland) · 2026Review
- The Utility of Artificial Intelligence in Dermatology Training and Practice: A National, Cross-Sectional Study.The Australasian journal of dermatology · 2026Article
- Beyond black-box AI: Comparing ChatGPT-4 interpretability and accuracy to CNNs in melanocytic lesions diagnosis.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Article
- Real-world diagnostic performance of artificial intelligence in skin cancer screening.Skin health and disease · 2026Article
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- Assessment of high- and low-risk histopathologic subtypes of basal cell carcinoma using artificial intelligence and dermatologist evaluation.JAAD international · 2026Article
- How good are artificial intelligence tools at identifying benign skin lesions? A systematic review and meta-analysis of the specificity of artificial intelligence tools in diagnosing suspicious skin lesions.Skin health and disease · 2026Review
- Artificial Intelligence in Healthcare Administration and Clinical Informatics: A Critical Review and Governance Roadmap.Healthcare (Basel, Switzerland) · 2026Review
- Article
- MultiTask learning AI system to assist BCC diagnosis with dual explanation.Scientific reports · 2026Article
- Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.Journal of medical Internet research · 2026Article
- ChatGPT, Gemini, and Claude in clinical and dermoscopic image analysis of basal cell carcinoma and its common mimickers: A comparative performance analysis.JID innovations : skin science from molecules to population health · 2026Article
- A quantitative analysis of global AI medical studies: gaps in randomized controlled trials.NPJ digital medicine · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
Scientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and meta-analysis to evaluate the performance of AI algorithms for skin cancer classification in comparison to clinicians with different levels of expertise. Based on PRISMA guidelines, 3 electronic databases (PubMed, Embase, and Cochrane Library) were screened for relevant articles up to August 2022. The quality of the studies was assessed using QUADAS-2. A meta-analysis of sensitivity and specificity was performed for the accuracy of AI and clinicians. Fifty-three studies were included in the systematic review, and 19 met the inclusion criteria for the meta-analysis. Considering all studies and all subgroups of clinicians, we found a sensitivity (Sn) and specificity (Sp) of 87.0% and 77.1% for AI algorithms, respectively, and a Sn of 79.78% and Sp of 73.6% for all clinicians (overall); differences were statistically significant for both Sn and Sp. The difference between AI performance (Sn 92.5%, Sp 66.5%) vs. generalists (Sn 64.6%, Sp 72.8%), was greater, when compared with expert clinicians. Performance between AI algorithms (Sn 86.3%, Sp 78.4%) vs expert dermatologists (Sn 84.2%, Sp 74.4%) was clinically comparable. Limitations of AI algorithms in clinical practice should be considered, and future studies should focus on real-world settings, and towards AI-assistance.
Identifiers
What OpenQuestion holds
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.