ArticleJAMA dermatology2019
Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis.
Article in JAMA dermatology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 6 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
44 citing papers in PubMed, 6 syntheses or guidelines pooled it, 122 citations in OpenAlex.
- Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis.The British 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
- Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Pooled it
- The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2021Pooled it
- Smartphone Applications in Dermatology With Special Focus on AI: A European-Continent-Based Review.International journal of dermatology · 2026Review
- 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
- DermaGPT a federated multimodal framework with a meta learned trust function for interpretable dermatology diagnostics.Scientific reports · 2026Article
- A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers.Scientific data · 2025Article
- Deep Learning With Optical Coherence Tomography for Melanoma Identification and Risk Prediction.Journal of biophotonics · 2025Article
- Artificial Intelligence in the Non-Invasive Detection of Melanoma.Life (Basel, Switzerland) · 2024Review
- The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- From diagnosis to intervention: a review of telemedicine's role in skin cancer care.Archives of dermatological research · 2024Review
- Review
- Exploring the feasibility of an artificial intelligence based clinical decision support system for cutaneous melanoma detection in primary care - a mixed method study.Scandinavian journal of primary health care · 2024Article
- An Automated Approach for Diagnosing Allergic Contact Dermatitis Using Deep Learning to Support Democratization of Patch Testing.Mayo Clinic proceedings. Digital health · 2024Article
- Skinly: A novel handheld IoT device for validating biophysical skin characteristics.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
- In vivo Study to Evaluate an Intelligent Algorithm for Time Efficient Detection of Malignant Melanoma Using Dermatofluoroscopy.Skin pharmacology and physiology · 2024Article
- Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease.NPJ digital medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
importanceThe recent advances in the field of machine learning have raised expectations that computer-aided diagnosis will become the standard for the diagnosis of melanoma.
objectiveTo critically review the current literature and compare the diagnostic accuracy of computer-aided diagnosis with that of human experts. DATA SOURCES: The MEDLINE, arXiv, and PubMed Central databases were searched to identify eligible studies published between January 1, 2002, and December 31, 2018. STUDY SELECTION: Studies that reported on the accuracy of automated systems for melanoma were selected. Search terms included melanoma, diagnosis, detection, computer aided, and artificial intelligence. DATA EXTRACTION AND SYNTHESIS: Evaluation of the risk of bias was performed using the QUADAS-2 tool, and quality assessment was based on predefined criteria. Data were analyzed from February 1 to March 10, 2019. MAIN OUTCOMES AND MEASURES: Summary estimates of sensitivity and specificity and summary receiver operating characteristic curves were the primary outcomes.
resultsThe literature search yielded 1694 potentially eligible studies, of which 132 were included and 70 offered sufficient information for a quantitative analysis. Most studies came from the field of computer science. Prospective clinical studies were rare. Combining the results for automated systems gave a melanoma sensitivity of 0.74 (95% CI, 0.66-0.80) and a specificity of 0.84 (95% CI, 0.79-0.88). Sensitivity was lower in studies that used independent test sets than in those that did not (0.51; 95% CI, 0.34-0.69 vs 0.82; 95% CI, 0.77-0.86; P < .001); however, the specificity was similar (0.83; 95% CI, 0.71-0.91 vs 0.85; 95% CI, 0.80-0.88; P = .67). In comparison with dermatologists' diagnosis, computer-aided diagnosis showed similar sensitivities and a 10 percentage points lower specificity, but the difference was not statistically significant. Studies were heterogeneous and substantial risk of bias was found in all but 4 of the 70 studies included in the quantitative analysis. CONCLUSIONS AND RELEVANCE: Although the accuracy of computer-aided diagnosis for melanoma detection is comparable to that of experts, the real-world applicability of these systems is unknown and potentially limited owing to overfitting and the risk of bias of the studies at hand.
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.