## 1 Introduction
We also explore clinical areas of dermatology where AI is utilised and examine its main achievements. We examine how AI improves the decision-making process in clinical dermatology. Additionally, mobile and telemedicine applications are discussed, which, thanks to AI, allow patients to perform initial assessments of skin lesions. Such solutionsdemonstrate how AI enhances the accessibility of dermatological care, supporting both patients and doctors in clinical practice. We focus on the ethical challenges that arise from implementing AI in dermatology. We explore the ethical issues associated with the use of AI in skin diagnostics. The issue of data privacy and informed patient consent is addressed. Dermatological images contain sensitive data; therefore, it is necessary to obtain explicit patient consent for the use of their images and ensure the anonymisation and security of these data. Algorithm bias and access fairness are also discussed: models trained primarily on images of people with light skin may achieve lower diagnostic accuracy in individuals with darker skin, thereby deepening inequalities in care. Another dilemma is legal responsibility and accountability--it is not clear who is responsible in the event of an incorrect diagnosis generated by AI (whether the fault lies with the software manufacturer, the doctor, or the medical facility), which gives rise to the need to clarify the legal rules in this area. We emphasise that, to maintain the trust of patients and doctors in new technologies, the transparency of algorithms (explainability) is necessary, as well as considering these ethical aspects from the outset, at the stage of designing and implementing AI systems. We also consider how AI models are verified in dermatology and the difficulties that they present. This work analyses methods of validating AI models and the challenges associated with testing them before clinical use. The quality of training data significantly impacts the credibility of models--limited diversity in skin images can lead to the overtraining of models and a decrease in their effectiveness outside laboratory conditions. The lack of full transparency (\"black box\") in neural network operation makes it challenging to understand the basis of algorithms' decisions, thereby complicating their reliable verification in clinical conditions. This article emphasises the need for the thorough testing of models, including testing their resistance to adversarial attacks (intentional image distortions) and assessing the explainability of their decisions, before they are implemented in routine diagnostics. However, note that the lack of uniform standards and legal requirements makes it difficult to enforce such detailed validation; legislation has not kept pace with the rapid development of AI, which means that manufacturers are not always obliged to test their models fully. In response, recommendations are proposed, including standardised model evaluation measures. The objectives of this paper are divided into two parts due to its cross-sectional and review nature. The main contribution is based on the methodology (Section 4) and focuses on a literature review on the aspects of (1) legal regulations, (2) verification, and (3) ethical issues regarding AI in dermatology, as well as (4) a bibliometric analysis of publications on AI in dermatology over the last 15 years, (5) an ongoing review of clinical applications, including mobile applications, and (6) an analysis of the neural network architectures and datasets used in the most frequently cited studies on skin diagnostics. This work is complemented by (7) a presentation of the main achievements in dermoscopic and histopathological diagnostics and (8) the formulation of recommendations for future research and technological development in dermatology. Section 2 describes the main machine and deep learning methods, including convolutional neural networks and transfer learning techniques, used to analyse skin images. Section 3 presents the process of modelling decision systems in clinical cases.