Artificial intelligence for melanoma diagnosis

Convolutional neural networks (CNN) have shown unprecedented accuracy in digital image analysis, which can be harnessed for melanoma recognition through automated evaluation of clinical and dermatoscopic images. In experimental studies, modern CNN architectures perform single image analysis at the level of dermatologists and domain-experts, also for multiclass predictions including a multitude of possible diagnoses. This may not necessarily translate to good clinical performance, and reliable randomized controlled prospective clinical trials for modern CNNs are essentially missing. Weaknesses of CNNs are that limitations of available training image datasets propagate to limitations of CNN predictions, and they cannot provide a reliable estimate of uncertainty. Recent research focuses on human-computer collaboration, where gains in accuracy were measured even with imperfect CNNs. With missing academic and clinical agreement on equivocal melanocytic lesions, fully automating histologic assessment of them with CNNs appear problematic, and applications in the near future are probably limited to supporting, referencing or recommendation roles.