Melanoma is among the most lethal forms of skin cancer, where early and accurate diagnosis significantly improves patient survival. Traditional diagnostic pathways, including clinical inspection and dermoscopy, are constrained by interobserver variability and limited access to expertise. Between 2020 and 2025, advances in artificial intelligence (AI) and medical imaging technologies have substantially redefined melanoma diagnostics. This narrative review synthesizes key developments in AI-based approaches, emphasizing the progression from convolutional neural networks to vision transformers and multimodal architectures that incorporate both clinical and imaging data. We examine the integration of AI with non-invasive imaging techniques such as reflectance confocal microscopy, high-frequency ultrasound, optical coherence tomography, and three-dimensional total body photography. The role of AI in teledermatology and mobile applications is also addressed, with a focus on expanding diagnostic accessibility. Persistent challenges include data bias, limited generalizability across diverse skin types, and a lack of prospective clinical validation. Recent regulatory frameworks, including the European Union Artificial Intelligence Act and the United States Food and Drug Administration’s guidance on adaptive systems, are discussed in the context of clinical deployment. The review concludes with perspectives on explainable AI, federated learning, and strategies for equitable implementation in dermatological oncology.
黑色素瘤是最致命的皮肤癌类型之一，早期精准诊断可显著提升患者生存率。传统诊断路径（包括临床检查与皮肤镜检查）受限于观察者间差异及专业资源可及性不足。2020至2025年间，人工智能与医学影像技术的进步从根本上重塑了黑色素瘤诊断体系。本文综述了人工智能诊断方法的核心进展，重点阐释了从卷积神经网络到视觉Transformer的演进过程，以及融合临床与影像数据的多模态架构。系统探讨了人工智能与反射式共聚焦显微镜、高频超声、光学相干断层扫描及三维全身摄影等无创影像技术的整合路径，同时剖析了人工智能在远程皮肤科诊疗及移动医疗应用中的拓展价值。当前领域仍面临数据偏差、跨肤色泛化能力有限及前瞻性临床验证不足等挑战。结合欧盟《人工智能法案》与美国食品药品监督管理局自适应系统指南等新兴监管框架，本文讨论了临床部署的关键问题。最后展望了可解释人工智能、联邦学习等技术在皮肤肿瘤学领域实现公平化应用的战略路径。
Artificial Intelligence and New Technologies in Melanoma Diagnosis: A Narrative Review
肿瘤(癌症)患者之家