ReviewCancers2024
Performance of Commercial Dermatoscopic Systems That Incorporate Artificial Intelligence for the Identification of Melanoma in General Practice: A Systematic Review.
Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- AI-Assisted Basal Cell Carcinoma Analysis in Line-Field Confocal OCT: Exploring Diagnostic Metrics.Journal of biophotonics · 2026Article
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review.Research square · 2026Article
- Multimodal artificial intelligence for enhanced skin cancer diagnosis and prognosis.Discover oncology · 2026Review
- Foundation-Model-Driven Skin Lesion Segmentation and Classification Using SAM-Adapters and Vision Transformers.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence and New Technologies in Melanoma Diagnosis: A Narrative Review.Cancers · 2025Review
- Clinical Application of Vision Transformers for Melanoma Classification: A Multi-Dataset Evaluation Study.Cancers · 2025Article
- Review
- Article
- Enhancing skin lesion classification: a CNN approach with human baseline comparison.PeerJ. Computer science · 2025Article
- Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review.Cancer medicine · 2025Article
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
backgroundCutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM.
methodsA systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases.
resultsA total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%.
conclusionInstead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians' work is supported by AI, facilitating management decisions and improving health outcomes.
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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.