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人工智能在皮肤病学中的进展与挑战:中国应用与前景综述

Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.

作者信息

Yu Jiaao, Cheong Io Hong, Kozlakidis Zisis, Wang Hui

机构信息

School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

School of Public Health, Shanghai Jiao Tong University, Shanghai, China.

出版信息

Front Digit Health. 2025 Aug 13;7:1544520. doi: 10.3389/fdgth.2025.1544520. eCollection 2025.

Abstract

The diagnosis of skin diseases can be challenging due to their diverse manifestations, while early detection of malignant skin cancers greatly improves the prognosis, highlighting the pressing need for efficient screening methods. In recent years, advancements in AI have paved the way for AI-aided diagnosis of skin lesions. Furthermore, the COVID-19 pandemic has spurred the demand of telemedicine, accelerating the integration of AI into medical domains, particularly in China. This article aims to provide an overview of the progress of AI-aided diagnosis in Chinese dermatology. Given the widespread use of public datasets in the reviewed studies, we compared the performance of AI models in segmentation and classification on public datasets. Despite the promising results of AI in experimental settings, we recognize the limitations of these public datasets in representing clinical scenarios in China. To address this gap, we reviewed the studies that used clinical datasets and conducted comparative analyses between AI and dermatologists. Although AI demonstrated comparable results to human experts, AI still cannot replace dermatologists due to limitations in generalizability and interpretability. We attempt to provide insights into improving the performance of AI through advancements in dataset quality, image pre-processing techniques, and integration of medical data. Finally, the role that AI will play in the medical practice and the relationship between AI and dermatologists are discussed. This systematic review addresses the gap in evaluating AI applications in Chinese dermatology, with a focus on dermatological datasets and real-world application.

摘要

由于皮肤疾病表现多样,其诊断具有挑战性,而恶性皮肤癌的早期检测能显著改善预后,这凸显了对高效筛查方法的迫切需求。近年来,人工智能的进步为皮肤病变的人工智能辅助诊断铺平了道路。此外,新冠疫情刺激了远程医疗的需求,加速了人工智能在医疗领域的整合,在中国尤其如此。本文旨在概述人工智能辅助诊断在中国皮肤科领域的进展。鉴于在综述研究中广泛使用公共数据集,我们比较了人工智能模型在公共数据集上的分割和分类性能。尽管人工智能在实验环境中取得了令人鼓舞的结果,但我们认识到这些公共数据集在代表中国临床场景方面存在局限性。为了弥补这一差距,我们回顾了使用临床数据集的研究,并对人工智能和皮肤科医生进行了比较分析。尽管人工智能显示出与人类专家相当的结果,但由于可推广性和可解释性的限制,人工智能仍然无法取代皮肤科医生。我们试图通过提高数据集质量、图像预处理技术以及整合医疗数据等方面的进展,为提升人工智能性能提供见解。最后,讨论了人工智能在医疗实践中将发挥的作用以及人工智能与皮肤科医生之间的关系。本系统综述弥补了评估人工智能在中国皮肤科应用方面的差距,重点关注皮肤科数据集和实际应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21a8/12380682/e40275a00577/fdgth-07-1544520-g001.jpg

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