## 7 Overview of the Major Applications of AI in Dermatology
### Core Applications
trained three CNNs to distinguish dermoscopic images of melanoma and benign nevi and test how minor image perturbations might compromise an AI pigmented lesion classification algorithm [179]. Minor image changes were tested. Two test sets had multiple lesion images. Zooms and rotations were the first \"artificial\" changes tested. The second test set had \"natural\" changes--slightly different lesion photos. The artificial changes increased the probability of a diagnostic class change from nevus to melanoma, from 3.5% to 12.2%. These findings suggest that AI is sensitive to image variables that the practitioner may overlook. CNNs can classify dermoscopic images of pigmented lesions on acral and mucosal surfaces. [PERSON] et al. tested a CNN's ability to distinguish acral, mucosa, and nail unit melanomas from benign lesions with similar localisation and morphology, such as solar lentigo and LMM. The CNN had AUCs of 0.926 (LMM), 0.928 (acral), and 0.989 (SSM) for melanoma subtypes. Mucosal and nail unit melanomas had AUCs of 0.754 and 0.621, respectively, indicating poor diagnostic accuracy [180]. [PERSON] et al. [181] compared the diagnostic accuracy of CNNs to that of two dermatologists with five or more years of experience in dermoscopy (expert group). Seven hundred and twenty-four images (350 acral melanoma, 374 benign nevi, histologically confirmed) were randomly divided into sets (A and B). Both datasets showed similar diagnostic accuracy between the CNN and the expert group (83.51% vs. 81.08%, 80.23%, and 81.64%). Both datasets had CNN and expert AUCs above 0.8. \"Non-expert\" general physicians had significantly lower diagnostic accuracy (62.71%) and AUCs (0.63-0.66). [PERSON] et al. [182] examined the decision-making accuracy of 60 physicians when presented with 100 dermoscopic images of acral pigmented lesions to demonstrate how AI can improve clinical assessment. Clinicians could biopsy or follow up on the lesion. The CNN's (ALMnet) diagnosis of acral nevus or lentiginous melanoma improved clinicians' decision-making compared to using history, dermoscopic images, and dermoscopic images alone (accuracy 86.9% vs. 79% vs. 74.7%). The CNN improved clinician concordance and reduced performance gaps within the group. The study presented by [PERSON] et al. [183] proposed a method for enhanced melanoma prediction on an unbalanced dataset by reconstructing an appropriate CNN architecture and optimising the algorithms. The contributions consisted of three essential elements: a custom loss function, custom mini-batch logic, and reformatted fully connected layers. In the experiment, the training dataset was kept up-to-date to include 17,302 images of melanoma and nevus. Based on the same dataset, the model's performance was compared to that of 157 dermatologists from 12 German university hospitals. The experimental results demonstrated that the proposed method outperformed all 157 dermatologists and the state-of-the-art method, with an area under the curve of 94.4%, sensitivity of 85%, and specificity of 95.5%. In addition, utilising the optimal threshold resulted in the most well-balanced measurement compared to other studies, and it has promising application potential in medical diagnosis, with sensitivity of 90% and specificity of 93.88%. The paper by [PERSON] et al. [184] presents the classification of images of skin lesions using machine learning and CNN techniques. Experiments were conducted using the HAM10000 dataset. Following the experiments, the machine learning and customised CNN techniques were evaluated based on the accuracy, precision, recall, and F1-score. The images were preprocessed before the training and testing phases, separated into feature and target values, and augmented with additional data. The results indicated that the customised CNN achieved a higher level of accuracy than the machine learning algorithms, at 95.1%. The proposed CNN performed better in classifying the HAM10000 dataset.