Dermatology Unit, University of Campania "Luigi Vanvitelli", Naples, Italy.
Dermatology Unit, University of Campania "Luigi Vanvitelli", Naples, Italy.
J Invest Dermatol. 2024 Mar;144(3):492-499. doi: 10.1016/j.jid.2023.10.004. Epub 2023 Nov 18.
The field of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms have the capacity to classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that leverages the expertise of both AI and human experts. However, it is important to note that most studies focus on assessing the diagnostic accuracy of AI in artificial settings rather than in real-world scenarios. Consequently, the practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown. Furthermore, there exists a knowledge gap concerning the optimal use cases and deployment settings for these AI systems as well as the practical challenges that may arise from widespread implementation. This review explores the advantages and limitations of AI in a variety of real-world contexts, with a specific focus on its value to consumers, general practitioners, and dermatologists.
皮肤癌检测领域为人工智能（AI）在基于图像的诊断医学领域的应用提供了一个引人注目的用例。通过对大型数据集的分析，人工智能算法能够以惊人的准确度对临床或皮肤镜图像进行分类。尽管这些基于 AI 的应用程序可以自主运行和在人工监督下运行，但通过利用 AI 和人类专家的专业知识的协作方法可以获得最佳结果。然而，需要注意的是，大多数研究都侧重于评估 AI 在人工环境中的诊断准确性，而不是在实际场景中。因此，AI 辅助诊断在临床环境中的实际效用在很大程度上仍然未知。此外，对于这些 AI 系统的最佳用例和部署设置以及广泛实施可能带来的实际挑战，还存在知识差距。本综述探讨了 AI 在各种真实环境中的优势和局限性，特别关注其对消费者、全科医生和皮肤科医生的价值。
