ArticleJournal of the American Academy of Dermatology2018
Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.
Article in Journal of the American Academy of Dermatology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 5 of them syntheses that pooled it.
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Who cites it
101 citing papers in PubMed, 5 syntheses or guidelines pooled it, 321 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis.BMC primary care · 2025Pooled it
- Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis.Frontiers in oncology · 2023Pooled it
- Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review.Mediators of inflammation · 2022Pooled it
- Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Establishment and Verification of a Prediction Model for Identifying Pathologic Infections Based on the Clinical Characteristics of Epidermoid Cysts.Surgical infections · 2022Trial
- Non-invasive clinical and microscopic evaluation of the response to treatment with clobetasol cream vs. calcipotriol/betamethasone dipropionate foam in mild to moderate plaque psoriasis: an investigator-initiated, phase IV, unicentric, open, randomized clinical trial.Journal of the European Academy of Dermatology and Venereology : JEADV · 2021Trial
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Artificial intelligence (AI)-assisted diagnosis of skin diseases: From image classification to dermatology-specific multimodal clinical reasoning.Global health & medicine · 2026Review
- Emerging Artificial Intelligence Models for Estimating Breslow Thickness from Dermoscopic Images.Biomedicines · 2026Review
- Automated triage of cancer-suspicious skin lesions with 3D total-body photography.NPJ digital medicine · 2025Article
- [Artificial intelligence in total body photography, digital dermoscopy and high-resolution dermoscopy].Dermatologie (Heidelberg, Germany) · 2025Review
- A deep learning-based dual-branch framework for automated skin lesion segmentation and classification via dermoscopic Images.Scientific reports · 2025Article
- Comparative Dermoscopic Analysis of Melanoma In Situ Versus Thin Invasive Melanoma Considering BRAF Mutational Status.Journal of clinical medicine · 2025Article
- Effect of patient-contextual skin images in human- and artificial intelligence-based diagnosis of melanoma: Results from the 2020 SIIM-ISIC melanoma classification challenge.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Article
- Transfer Learning-Based Ensemble of CNNs and Vision Transformers for Accurate Melanoma Diagnosis and Image Retrieval.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks.Discover oncology · 2025Article
- Building Better Deep Learning Models Through Dataset Fusion: A Case Study in Skin Cancer Classification with Hyperdatasets.Diagnostics (Basel, Switzerland) · 2025Article
- International Skin Imaging Collaboration-Designated Diagnoses (ISIC-DX): Consensus terminology for lesion diagnostic labeling.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Article
- Artificial Intelligence in the Non-Invasive Detection of Melanoma.Life (Basel, Switzerland) · 2024Review
41 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 7 institutions in 3 countries.
Funding
Abstract
backgroundComputer vision may aid in melanoma detection.
objectiveWe sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images.
methodsWe conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant.
resultsThe average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001). LIMITATIONS: The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice.
conclusionDeep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists.
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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.