ArticleCureus2024
Automated Machine Learning (AutoML) for the Diagnosis of Melanoma Skin Lesions From Consumer-Grade Camera Photos.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Marketing-AutoM3L: domain-aware automated machine learning for financial customer analytics.Frontiers in artificial intelligence · 2026Article
- Artificial Intelligence in the Non-Invasive Detection of Melanoma.Life (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn recent years, there has been much speculation about the role of artificial intelligence (AI) and machine learning in dermatology. Advances in computer vision have increased the potential for automated diagnosis of images. However, there remains a gap between the technological development of the algorithms and their real-world implementation. This study aims to develop and test an automated machine learning (AutoML) algorithm for the diagnosis of melanoma, with no technical or coding skills required by the operator.
methodsThe Skin Cancer Detection Dataset from the University of Waterloo Vision and Image Processing Lab contains 206 images sourced from the public databases DermIS and DermQuest. The dataset was split into two groups: training data (n=174) and testing data (n=32). A machine learning algorithm was created using 'Teachable Machine', trained on the training data, to differentiate between melanoma and non-melanoma skin lesions.
resultsThe AutoML algorithm identified 12/14 non-melanoma images and 15/18 melanoma images in the testing dataset. The overall accuracy was 84.4%, with a sensitivity of 83.3% and a specificity of 85.7%.
conclusionsExisting literature has tested a range of different machine learning algorithms on the same dataset. These have often required expertise in machine learning and the ability to code. The results of this study, using a no-code tool, perform comparably to existing efforts and suggest that there is potential for future clinical AI algorithms to be developed by doctors even without any technical expertise as long as they have access to relevant local data.
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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.