标题： 生成式人工智能在医学影像中的应用：基础、进展与临床转化 显示英文标题

标题： Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation

摘要： 生成式人工智能（AI）正在迅速改变医学影像，通过实现数据合成、图像增强、模态转换和时空建模等能力。 本综述全面而前瞻性地总结了生成建模的最新进展，包括生成对抗网络（GANs）、变分自编码器（VAEs）、扩散模型以及新兴的多模态基础架构，并评估了它们在临床影像连续体中的扩展作用。 我们系统地检查了生成式AI如何对影像工作流程的关键阶段做出贡献，从采集和重建到跨模态合成、诊断支持和治疗计划。 重点放在回顾性和前瞻性临床场景中，其中生成模型有助于解决长期存在的挑战，如数据稀缺性、标准化以及模态间的整合。 为了促进严格的基准测试和转化准备，我们提出一个包含像素级保真度、特征级真实性和任务级临床相关性的三层评估框架。 我们还识别了现实世界部署中的关键障碍，包括在领域转移下的泛化能力、幻觉风险、数据隐私问题和监管障碍。 最后，我们探讨了生成式AI与大规模基础模型的融合，强调这种协同作用可能使下一代可扩展、可靠且临床集成的影像系统成为可能。 通过绘制技术进展和转化路径，本综述旨在指导未来的研究，并在人工智能、医学和生物医学工程的交叉领域促进跨学科合作。 显示英文摘要

摘要： Generative artificial intelligence (AI) is rapidly transforming medical imaging by enabling capabilities such as data synthesis, image enhancement, modality translation, and spatiotemporal modeling. This review presents a comprehensive and forward-looking synthesis of recent advances in generative modeling including generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and emerging multimodal foundation architectures and evaluates their expanding roles across the clinical imaging continuum. We systematically examine how generative AI contributes to key stages of the imaging workflow, from acquisition and reconstruction to cross-modality synthesis, diagnostic support, and treatment planning. Emphasis is placed on both retrospective and prospective clinical scenarios, where generative models help address longstanding challenges such as data scarcity, standardization, and integration across modalities. To promote rigorous benchmarking and translational readiness, we propose a three-tiered evaluation framework encompassing pixel-level fidelity, feature-level realism, and task-level clinical relevance. We also identify critical obstacles to real-world deployment, including generalization under domain shift, hallucination risk, data privacy concerns, and regulatory hurdles. Finally, we explore the convergence of generative AI with large-scale foundation models, highlighting how this synergy may enable the next generation of scalable, reliable, and clinically integrated imaging systems. By charting technical progress and translational pathways, this review aims to guide future research and foster interdisciplinary collaboration at the intersection of AI, medicine, and biomedical engineering.