Abstract：
In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.
摘要：
近年来,使用人工智能算法对色素性皮肤病变进行分类的准确性有了显著提高。智能分析和分类系统明显优于皮肤科医生和肿瘤学家使用的视觉诊断方法。然而,由于缺乏通用性和潜在错误分类的风险，此类系统在临床实践中的应用受到严重限制。在临床病理实践中成功实施基于人工智能的工具需要对现有模型的有效性和性能进行全面研究，以及潜在研究发展的进一步有希望的领域。本系统综述的目的是调查和评估人工智能技术用于检测色素性皮肤病变的恶性形式的准确性。对于这项研究，从电子科学出版商中选择了10,589篇科学研究和评论文章，其中171篇文章被纳入本系统综述。所有选定的科学文章都根据所提出的神经网络算法从机器学习到多模态智能架构进行分发，并在手稿的相应部分进行了描述。这项研究旨在探索自动皮肤癌识别系统，从简单的机器学习算法到基于高级编码器-解码器模型的多模态集成系统，视觉变压器（ViT），以及生成和尖峰神经网络。此外,作为分析的结果，未来的研究方向，前景,并讨论了进一步开发用于对色素性皮肤病变进行分类的自动神经网络系统的潜力。
