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

2025-08-07

AI Summary

This study addresses core challenges impeding clinical deployment of generative AI in medical imaging—namely, data scarcity, modality heterogeneity, and insufficient standardization. We propose an end-to-end application framework spanning image generation, augmentation, cross-modal translation, and clinical decision support. Methodologically, we introduce a novel three-tiered evaluation paradigm (“pixel–feature–task”) that jointly assesses fidelity, anatomical plausibility, and clinical utility; integrate GANs, VAEs, diffusion models, and multimodal foundation models; and incorporate image reconstruction, synthetic data generation, and spatiotemporal modeling techniques. Experimental results demonstrate substantial improvements in synthetic image quality and generalizability, significant reduction in annotation dependency, enhanced model robustness across multi-center and multi-device settings, and a verifiable, regulation-compliant pathway for clinical deployment.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• Addressing data scarcity in medical imaging using generative AI
• Improving clinical workflow with AI-driven image enhancement and synthesis
• Overcoming domain shift and privacy issues in AI deployment

Innovation

• Generative AI enhances medical imaging workflows
• GANs, VAEs, diffusion models for clinical imaging
• Three-tiered framework evaluates clinical relevance