In comparison with inspection with the unaided eye, dermoscopy (dermatoscopy¹) improves the accuracy of the diagnosis of pigmented skin lesions.² The improvement of dermoscopy is most evident for small and inconspicuous melanomas³ and for pigmented basal cell carcinoma.⁴ Because dermoscopic criteria are more specific and the number of differential diagnoses is significantly lower, pigmented skin lesions are easier to diagnose than nonpigmented lesions. The most common types of skin cancers, however, are usually nonpigmented. A previous study showed that dermoscopy also improves the accuracy of the diagnosis of nonpigmented lesions, although the improvement was less pronounced than for pigmented lesions.⁵ The proportion of correct diagnoses by expert raters increased from 41.3% with the unaided eye to 52.7% with dermoscopy. The improvement of nonexperts was less pronounced.

The rising incidence of melanoma, the benefits of early diagnosis, and the limited access to dermatologic services in some countries entailed increased efforts to develop diagnostic systems that are independent of human expertise. Most systems fall into the category of image-based, automated diagnostic systems and use either clinical or dermoscopic images. The hope is that computer-aided diagnosis (CAD) could provide decision support for physicians or could screen large numbers of images for teleconsultation services. Early studies on CAD of skin lesions relied on hand-crafted feature engineering and segmentation masks. These methods showed promising results and reached a diagnostic accuracy comparable to human ratings in experimental settings.¹ The accuracy of the automated diagnostic system in real-life settings in the only prospective controlled trial to date was lower than expected.² Recent advances in computer science³ and the introduction of convolutional neural networks and deep-learning–based approaches revolutionized the classification of medical image analysis.⁴ Since the last meta-analysis that was published in 2003,¹ a significant number of new studies have been published on this topic, but to our knowledge, there is no study that summarizes the body of literature. The aims of this meta-analysis were to critically review the current literature on CAD for melanoma, evaluate the diagnostic accuracy in comparison with that of dermatologists, analyze the association between methodologic differences and performance measures, and explore the applicability of CAD in real-world settings.

Opinion statement Dermatoscopy (dermoscopy) improves the diagnosis of benign and malignant cutaneous neoplasms in comparison with examination with the unaided eye and should be used routinely for all pigmented and non-pigmented cutaneous neoplasms. It is especially useful for the early stage of melanoma when melanoma-specific criteria are invisible to the unaided eye. Preselection by the unaided eye is therefore not recommended. The increased availability of polarized dermatoscopes, and the extended use of dermatoscopy in non-pigmented lesions led to the discovery of new criteria, and we recommend that lesions should be examined with polarized and non-polarized dermatoscopy. The “chaos and clues algorithm” is a good starting point for beginners because it is easy to use, accurate, and it works for all types of pigmented lesions not only for those melanocytic. Physicians, who use dermatoscopy routinely, should be aware of new clues for acral melanomas, nail matrix melanomas, melanoma in situ, and nodular melanoma. Dermatoscopy should also be used to distinguish between different subtypes of basal cell carcinoma and to discriminate highly from poorly differentiated squamous cell carcinomas to optimize therapy and management of non-melanoma skin cancer. One of the most exciting areas of research is the use of dermatoscopic images for machine learning and automated diagnosis. Convolutional neural networks trained with dermatoscopic images are able to diagnose pigmented lesions with the same accuracy as human experts. We humans should not be afraid of this new and exciting development because it will most likely lead to a peaceful and fruitful coexistence of human experts and decision support systems.