## 1 Introduction  
Artificial intelligence (AI) aims to simulate human cognitive function, representing a paradigm shift in healthcare. In the future, the increasing complexity and growth of healthcare data will necessitate the development of more AI applications. Healthcare providers and biomedical companies are utilising multiple AI types [1]. AI dates back to the 1950s, when British mathematician [PERSON] questioned whether machines could think. Over a decade later, AI was integrated into the life sciences [2], and, in the 1970s, it began to enter the healthcare industry [3]. AI increased its presence in clinical settings in the 1980s and beyond through artificial neural networks, Bayesian networks, and hybrid intelligent systems. Healthcare only recently became the dominant industrial application of AI in aggregate equity funding [4]. Physical AI (e.g., surgical robots in the real world) and virtual AI (e.g., intelligent applications in a virtual environment) are now utilised to support patients and healthcare professionals. Dermatology is a field that relies on morphological characteristics, and the majority of diagnoses are based on the recognition of visual patterns. Dermatology is ideally suited for assisted diagnosis using AI image recognition capabilities. Dermoscopy, very high-frequency (VHF) ultrasound, and reflectance confocal microscopy (RCM) are examples of skin imaging technologies. Artificial intelligence (AI)'s integration into dermatology has gained significant attention recently, revolutionising the field and enhancing patient care [5]. For example, this has enabled patients with skin lesions to access an application for self-checking (e.g., [6]). The mentioned application states that it has 943,569 AI Dermatologist users, 2,935,707 online checks have been conducted, and 35,755 skin diseases have been identified. The history of AI in dermatology dates back to the early 2000s, with numerous advancements made since then. The field of dermatology has seen significant breakthroughs thanks to AI, which has revolutionised how skin problems are identified, treated, and managed [7; 8]--e.g., AI is better than dermatologists at diagnosing skin cancer and helps to improve doctors' work in cooperation with the healthcare system [9]. Applying AI technology in dermatology can enhance the diagnostic precision, improve the clinical throughput, and yield better outcomes for patients. In the early 2000s, researchers began exploring the application of machine learning algorithms in dermatology. Artificial neural networks (ANNs) have been developed in recent decades for various medical applications. However, their application within the dermatology field still needs improvement. The primary application of ANNs in dermatology is in distinguishing between benign and malignant pigmented lesions in vivo [10]. In 2006, the \"deep learning\" concept and its training method were introduced [11]. Deep learning enables computational models with multiple processing layers to discover data representations with multiple levels of abstraction. Deep learning, a subfield of artificial intelligence, has shown significant success in dermatology over the past few years. Such models, including convolutional neural networks (CNNs), can directly learn intricate patterns and characteristics from vast datasets. The review aims to address several key research questions related to the application of artificial intelligence (AI) in dermatology. We present the types of AI methods and algorithms used in dermatology. We examine the basic AI methods employed in dermatological diagnostics, with a particular focus on deep learning and neural networks. We emphasise that deep neural networks (e.g., convolutional neural networks) have become key for the advanced analysis of skin images and the automatic interpretation of skin lesions. We also discuss other machine learning approaches (e.g., supervised, unsupervised, and reinforcement learning methods) as a context for the development of AI in dermatology.