Generalizability and usefulness of artificial intelligence for skin cancer diagnostics: An algorithm validation study

Background Artificial intelligence can be trained to outperform dermatologists in image‐based skin cancer diagnostics. However, the networks' sensitivity to biases and overfitting may hamper their clinical applicability. Objectives The aim of this study was to explain the potential consequences...

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Bibliographic Details
Published in:JEADV clinical practice 2022-12, Vol.1 (4), p.344-354
Main Authors: Ternov, Niels K., Christensen, Anders N., Kampen, Peter J. T., Als, Gustav, Vestergaard, Tine, Konge, Lars, Tolsgaard, Martin, Hölmich, Lisbet R., Guitera, Pascale, Chakera, Annette H., Hannemose, Morten R.
Format: Article
Language:English
Subjects:
Algorithms
Archives & records
Artificial intelligence
Automation
Data integrity
Datasets
Melanoma
Simulation
Skin cancer
skin cancer prevention and early detection
Validation studies
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Summary:Background Artificial intelligence can be trained to outperform dermatologists in image‐based skin cancer diagnostics. However, the networks' sensitivity to biases and overfitting may hamper their clinical applicability. Objectives The aim of this study was to explain the potential consequences of implementing convolutional neural networks for stand‐alone melanoma diagnostics and skin lesion triage. Methods In this algorithm validation study on retrospective data, we reproduced and evaluated the performance of state‐of‐the‐art artificial intelligence (convolutional neural networks) for skin cancer diagnostics. The networks were trained on 25,331 annotated dermoscopic skin lesion images from an open‐source data set (ISIC‐2019) and tested using a novel data set (AISC‐2021) consisting of 26,591 annotated dermoscopic skin lesion images. We tested the trained algorithms' ability to generalize to new data and their diagnostic performance in two simulations (melanoma diagnostics and skin lesion triage). Results The trained algorithms performed significantly less accurate diagnostics on images of nevi, melanomas and actinic keratoses from the AISC‐2021 data set than the ISIC‐2019 data set (p 
ISSN:2768-6566
2768-6566
DOI:10.1002/jvc2.59