Researchers have been investigating the use of artificial intelligence (AI) to optimize or boost the latest testing procedures for skin cancer. Nasr-Esfahani et al. were the first to train a neural network for melanoma detection, and their proposed method had a sensitivity and specificity of 0.81 and 0.80, respectively. Stanford University published a study on deep learning of malignant tumours in 2017. They demonstrated skin lesion categorization using a single convolutional neural network. It was trained end-to-end from images using only pixel resolution and illnesses labels as inputs. A convolutional network was trained using 129,450 clinical images representing 2,032 distinct diseases. The effectiveness of the system was evaluated in comparison to 21 board-certified dermatologists using biopsy-verified clinical images and two crucial binary classifications of cases: keratinocyte squamous cell carcinoma versus benign seborrheic keratoses and malignant melanomas versus benign nevi. The first case involved identifying the most common cancers, while the second involved identifying the deadliest skin cancer. It was discovered that the machine could identify and classify skin cancer with the same accuracy as board-certified dermatologists. This was a pivotal publication on the use of AI in dermatology. However, because they also do not include demographic information, the validation of their work is questionable.

Although it was widely acknowledged applying deep-learning technologies to skin cancer, could improve the high specificity of skin cancer screening too. It was assumed the number of training images required for such a practice could become enormous.