ArticleDiagnostics (Basel, Switzerland)2020
Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
What it found
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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
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- 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
- Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.Journal of medical Internet research · 2023Pooled it
- New Trends in Melanoma Detection Using Neural Networks: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Diagnostic Accuracy of Artificial Intelligence Applications on a Diverse Skin Image Set.Cureus · 2026Article
- Deep Ensemble Learning for Multiclass Skin Lesion Classification.Bioengineering (Basel, Switzerland) · 2025Article
- AI and Primary Care: Scoping Review.Journal of medical Internet research · 2025Article
- Deep Learning-Based Classification System for Facial Pigmented Lesions to Aid Laser Treatment Decisions.Cureus · 2025Article
- Advances in computer vision and deep learning-facilitated early detection of melanoma.Briefings in functional genomics · 2025Review
- AI-Driven Enhancement of Skin Cancer Diagnosis: A Two-Stage Voting Ensemble Approach Using Dermoscopic Data.Cancers · 2025Article
- The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis.NPJ digital medicine · 2024Review
- Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis.NPJ digital medicine · 2024Review
- Issues in Melanoma Detection: Semisupervised Deep Learning Algorithm Development via a Combination of Human and Artificial Intelligence.JMIR dermatology · 2022Article
- Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends.Journal of clinical medicine · 2022Review
- Melanoma Detection Using XGB Classifier Combined with Feature Extraction and K-Means SMOTE Techniques.Diagnostics (Basel, Switzerland) · 2022Article
- InSiNet: a deep convolutional approach to skin cancer detection and segmentation.Medical & biological engineering & computing · 2022Article
- Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review.JAMA dermatology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study evaluated whether deep learning frameworks trained in large datasets can help non-dermatologist physicians improve their accuracy in categorizing the seven most common pigmented skin lesions. Open-source skin images were downloaded from the International Skin Imaging Collaboration (ISIC) archive. Different deep neural networks (DNNs) (
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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.