ArticleJournal of the European Academy of Dermatology and Venereology : JEADV2021
Performance of a deep neural network in teledermatology: a single-centre prospective diagnostic study.
Article in Journal of the European Academy of Dermatology and Venereology : JEADV, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 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
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 65 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
- Natural language processing in dermatology: A systematic literature review and state of the art.Journal of the European Academy of Dermatology and Venereology : JEADV · 2024Pooled it
- Perilesional sun damage as a diagnostic clue for pigmented actinic keratosis and Bowen's disease.Journal of the European Academy of Dermatology and Venereology : JEADV · 2021Trial
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- 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
- Prospective real-world implementation of deep learning systems in healthcare: a systematic review guided by implementation science.NPJ digital medicine · 2026Article
- Planet-wide performance of a skin disease AI algorithm validated in Korea.NPJ digital medicine · 2025Article
- Patient Perceptions of Artificial Intelligence and Telemedicine in Dermatology: Narrative Review.JMIR dermatology · 2025Review
- 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
- Review
- Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma.Nature communications · 2024Article
- Artificial intelligence and skin cancer.Frontiers in medicine · 2024Review
- Review of Teledermatology: Lessons Learned from the COVID-19 Pandemic.American journal of clinical dermatology · 2024Review
- Using green background for dermatological images to improve deep learning-based image classification.Archives of dermatological research · 2023Article
- Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet.JAMA dermatology · 2023Article
- The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks.International journal of environmental research and public health · 2023Review
- Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
20 authors at 4 institutions in 4 countries.
Funding
Abstract
backgroundThe use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not been yet tested in real-life conditions.
objectiveTo assess the diagnostic performance and potential clinical utility of a 174-multiclass AI algorithm in a real-life telemedicine setting.
methodsProspective, diagnostic accuracy study including consecutive patients who submitted images for teledermatology evaluation. The treating dermatologist chose a single image to upload to a web application during teleconsultation. A follow-up reader study including nine healthcare providers (3 dermatologists, 3 dermatology residents and 3 general practitioners) was performed.
resultsA total of 340 cases from 281 patients met study inclusion criteria. The mean (SD) age of patients was 33.7 (17.5) years; 63% (n = 177) were female. Exposure to the AI algorithm results was considered useful in 11.8% of visits (n = 40) and the teledermatologist correctly modified the real-time diagnosis in 0.6% (n = 2) of cases. The overall top-1 accuracy of the algorithm (41.2%) was lower than that of the dermatologists (60.1%), residents (57.8%) and general practitioners (49.3%) (all comparisons P < 0.05, in the reader study). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained, the balanced top-1 accuracy of the algorithm (47.6%) was comparable to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%; P = 0.049). Algorithm performance was associated with patient skin type and image quality.
conclusionsA 174-disease class AI algorithm appears to be a promising tool in the triage and evaluation of lesions with patient-taken photographs via telemedicine.
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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.