人工智能在皮肤科的进展与挑战:在中国的应用与展望。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2025-08-13 eCollection Date: 2025-01-01 DOI:10.3389/fdgth.2025.1544520
Jiaao Yu, Io Hong Cheong, Zisis Kozlakidis, Hui Wang
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引用次数: 0

摘要

由于皮肤疾病的多种表现,其诊断可能具有挑战性,而恶性皮肤癌的早期发现可大大改善预后,因此迫切需要有效的筛查方法。近年来,人工智能的进步为人工智能辅助皮肤病变诊断铺平了道路。此外,COVID-19大流行刺激了远程医疗的需求,加速了人工智能与医疗领域的整合,特别是在中国。本文就人工智能辅助诊断在中国皮肤科的研究进展作一综述。鉴于在回顾的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的局限性。为了解决这一差距,我们回顾了使用临床数据集的研究,并在人工智能和皮肤科医生之间进行了比较分析。尽管人工智能显示出与人类专家相当的结果,但由于通用性和可解释性的限制,人工智能仍然无法取代皮肤科医生。我们试图通过在数据集质量、图像预处理技术和医疗数据集成方面的进步,为提高人工智能的性能提供见解。最后,讨论了人工智能在医疗实践中的作用以及人工智能与皮肤科医生之间的关系。本系统综述解决了人工智能在中国皮肤病学应用评估中的差距,重点关注皮肤病学数据集和现实世界的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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.

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