## 7 Overview of the Major Applications of AI in Dermatology
### Core Applications  
In recent years, the incorporation of artificial intelligence (AI) has substantially transformed various fields, including dermatology, in terms of diagnostic accuracy and the development of new treatments. The intersection of AI and dermatology has yielded numerous innovative applications with the potential to revolutionise the diagnosis, treatment, and management of skin conditions. From detecting skin cancer to recommending personalised treatments, AI has demonstrated its ability to improve precision, efficiency, and patient outcomes. AI in dermatology has been developing for a long time thanks to the advancement of digitisation and technology [164; 165; 166], primarily driven by new image processing algorithms and the development of convolutional neural networks [168]. Advancements in hardware development have also facilitated this progress. Regarding treatment, AI can select the optimal treatment for patients, determine the number of treatments needed, and assess the effectiveness of treatments for those with skin diseases [169; 170]. In addition, AI-based surgical robotic systems can help to reduce human resource consumption, eliminate human fatigue and potential errors, and dramatically reduce surgery times, thereby improving surgical treatment [171; 172]. The detection of skin cancer using artificial intelligence (AI) has emerged as a promising application, enabling dermatologists to make early diagnoses and improving patient outcomes. AI algorithms, particularly deep learning models, have been developed to analyse images of skin lesions and detect signs of skin cancer. Below is an overview of how AI is being used to detect skin cancer. Clinical photography, or confocal microscopy, can acquire images of skin lesions, which artificial intelligence algorithms can then analyse. AI systems can assist in triaging patients by evaluating the risk levels of cutaneous lesions. These systems can classify lesions into various risk categories based on their characteristics, assisting dermatologists with case prioritisation and resource allocation. Artificial intelligence can provide dermatologists with decision support by providing a second opinion or additional information during the diagnostic process. Recent studies have compared CNNs and humans in categorising dermoscopic images as melanoma or non-melanoma [173; 174; 175; 176]. A systematic review found eleven studies comparing CNNs to human experts in the dermoscopic image classification of pigmented lesions [177]. AI can categorise pigmented skin lesions in humans regardless of the dataset, methodology, or outcome measure. AI classified dermoscopic images as nevi, melanoma, or seborrheic keratoses with an AUC of 0.87, compared to 0.74 for dermatologists. The AI algorithm had 85% specificity, compared to 72.6% for dermatologists, as well as 76% sensitivity. In a separate comparison of AI versus 58 dermatologists using dermoscopic images to detect melanoma, the dermatologists achieved sensitivity of 86.6% and specificity of 71.3%, whereas the CNN achieved 82.5% specificity. With clinical information and images, the method developers improved their sensitivity (88.9%) and specificity (75.7%) but fell short in comparison to the CNN. The CNN's AUC was 0.86, while the dermatologists' was 0.79 [174]. Several studies have demonstrated how AI can enhance the dermoscopic evaluation of skin lesions. [PERSON] et al. used AI to classify lesions for which physicians had low diagnostic confidence [178]. Notably, 26.6% of dermatologists and 51% of dermatology residents had low confidence. AI helped dermatologists (\(n\) = 905) and dermatology residents (\(n\) = 981) to correctly classify lesion evaluations, increasing the rates from 73.4% to 75.4% and 69.4% to 72.6%, respectively [178]. To better understand the lesion morphology, the human examiner employs variable lighting and perspectives (e.g., head-on view, side view). AI algorithms use static images. [PERSON] et al.