ArticleDermatology practical & conceptual2020
Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy.
Article in Dermatology practical & conceptual, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence technologies for assessing skin lesions for referral on the urgent suspected cancer pathway to detect benign lesions and reduce secondary care specialist appointments: early value assessment.Health technology assessment (Winchester, England) · 2026Pooled it
- Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions.JAMA network open · 2019Trial
- Cost-Effectiveness of an Artificial Intelligence as a Medical Device (AIaMD) for Triaging Patients Presenting to Primary Care with Concerns that They Have a Skin Cancer: A Modelling Study.PharmacoEconomics - open · 2026Article
- Article
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- Prospective multicenter study using artificial intelligence to improve dermoscopic melanoma diagnosis in patient care.Communications medicine · 2024Article
- Artificial Intelligence in the Diagnosis of Onychomycosis-Literature Review.Journal of fungi (Basel, Switzerland) · 2024Review
- The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Stakeholder perspectives towards diagnostic artificial intelligence: a co-produced qualitative evidence synthesis.EClinicalMedicine · 2024Review
- Accuracy of an artificial intelligence as a medical device as part of a UK-based skin cancer teledermatology service.Frontiers in medicine · 2024Article
- Technology innovation to reduce health inequality in skin diagnosis and to improve patient outcomes for people of color: a thematic literature review and future research agenda.Frontiers in artificial intelligence · 2024Review
- Review
- Review
- Advances in Early Detection of Melanoma and the Future of At-Home Testing.Life (Basel, Switzerland) · 2023Review
- Slit lamp polarized dermoscopy: a cost-effective tool to assess eyelid lesions.International ophthalmology · 2023Article
- Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance.Frontiers in medicine · 2023Article
- New AI-algorithms on smartphones to detect skin cancer in a clinical setting-A validation study.PloS one · 2023Article
- An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset.Diagnostics (Basel, Switzerland) · 2022Article
- Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review.JAMA dermatology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMalignant melanoma can most successfully be cured when diagnosed at an early stage in the natural history. However, there is controversy over screening programs and many advocate screening only for high-risk individuals.
objectivesThis study aimed to evaluate the accuracy of an artificial intelligence neural network (Deep Ensemble for Recognition of Melanoma [DERM]) to identify malignant melanoma from dermoscopic images of pigmented skin lesions and to show how this compared to doctors' performance assessed by meta-analysis.
methodsDERM was trained and tested using 7,102 dermoscopic images of both histologically confirmed melanoma (24%) and benign pigmented lesions (76%). A meta-analysis was conducted of studies examining the accuracy of naked-eye examination, with or without dermoscopy, by specialist and general physicians whose clinical diagnosis was compared to histopathology. The meta-analysis was based on evaluation of 32,226 pigmented lesions including 3,277 histopathology-confirmed malignant melanoma cases. The receiver operating characteristic (ROC) curve was used to examine and compare the diagnostic accuracy.
resultsDERM achieved a ROC area under the curve (AUC) of 0.93 (95% confidence interval: 0.92-0.94), and sensitivity and specificity of 85.0% and 85.3%, respectively. Avoidance of false-negative results is essential, so different decision thresholds were examined. At 95% sensitivity DERM achieved a specificity of 64.1% and at 95% specificity the sensitivity was 67%. The meta-analysis showed primary care physicians (10 studies) achieve an AUC of 0.83 (95% confidence interval: 0.79-0.86), with sensitivity and specificity of 79.9% and 70.9%; and dermatologists (92 studies) 0.91 (0.88-0.93), 87.5%, and 81.4%, respectively.
conclusionsDERM has the potential to be used as a decision support tool in primary care, by providing dermatologist-grade recommendation on the likelihood of malignant melanoma.
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