Machine learning and melanoma: The future of screening

<p>Lentigo Maligna: Clinical Presentation and Appropriate Management</p>

“…71 Deep learning convolutional neural networks (CNNs) have further improved the accuracy of ML in melanoma screening, even exceeding some dermatologists. 71,72 These algorithms could improve LM/ LMM diagnosis in the future, 73 although some limitations have to be addressed. Winkler et al 74 investigated the diagnostic performance of a CNN across different melanoma subtypes, including LMM.…”

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

“…Most of these cover the potential use of AI in differentiating between benign and malignant skin lesions. For example, studies have reviewed the specificities and sensitivities of AI tools for melanoma screening (9). To the best of our knowledge, only one systematic review has been published on dermatological applications of AI in general, not limited to neoplastic lesions (10).…”

“…These applications are typically validated by comparing their ability to correctly diagnose lesions to the ability of certified dermatologists (32). One review of photo recognition applications by Safran et al included 48 melanoma-screening tools and demonstrated a mean sensitivity of 87.60% and a mean specificity of 83.54% (9). Interest toward this topic has grown to the extent that an international skin imaging competition was founded in 2016 and has been occurring annually since (32,35).…”

Diagnosis and Management of Lentigo Maligna: Clinical Presentation and Comprehensive Review

“…Deep learning convolutional neural networks (CNNs) have improved ML's melanoma screening performance even further, outperforming some dermatologists [ 59 ]. Although certain shortcomings have to be resolved, these algorithms can enhance LM/LMM diagnosis in the future [ 60 ]. A CNN was used by Winkler et al [ 61 ] to diagnose different melanoma subtypes, including LMM.…”