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

TL;DR

This paper reviews recent advances in generative AI for medical imaging, highlighting its potential to improve clinical workflows, address data challenges, and facilitate future integration with foundation models.

Contribution

It provides a comprehensive synthesis of generative modeling techniques, evaluates their clinical applications, and proposes a framework for benchmarking and translational progress.

Findings

Generative AI enhances data synthesis and image quality in medical imaging.

A three-tiered evaluation framework for clinical relevance is proposed.

Identifies key challenges like domain shift and regulatory hurdles.

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.