Abstract
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.
Abstract (translated)
生成式人工智能（AI）正在通过实现数据合成、图像增强、模态转换和时空建模等功能，迅速改变医学成像领域。这篇综述全面而前瞻地总结了近年来在生成模型方面取得的最新进展，包括生成对抗网络（GANs）、变分自动编码器（VAEs）、扩散模型以及新兴多模式基础架构，并评估它们在整个临床影像链中的扩展作用。我们系统性地考察了生成式AI如何助力成像工作流程的关键阶段，从获取和重建到跨模态合成、诊断支持及治疗计划制定。该综述特别强调回顾性和前瞻性临床场景，在这些场景中，生成模型帮助解决长期存在的挑战，如数据不足、标准化以及不同模态之间的整合问题。 为了促进严格的基准测试和转化准备度，我们提出了一级评估框架，涵盖像素级保真度、特征级现实感及任务级的临床相关性。同时，我们也指出了将生成式AI应用于真实世界部署的关键障碍，包括在领域转移下的泛化能力、幻觉风险、数据隐私问题以及监管障碍。 最后，本文探讨了生成式AI与大规模基础模型融合的趋势，并强调这种协同作用如何可能催生下一代可扩展、可靠且临床集成的成像系统。通过绘制技术进步和转化路径，本综述旨在指导未来的研究并促进人工智能、医学及生物医学工程交叉领域的合作。
URL
https://arxiv.org/abs/2508.09177
