A systematic review of generative AI approaches for medical image enhancement: Comparing GANs, transformers, and diffusion models
Abstract
BACKGROUND: Medical imaging is a vital diagnostic tool that provides detailed insights into human anatomy but faces challenges affecting its accuracy and efficiency. Advanced generative AI models offer promising solutions. Unlike previous reviews with a narrow focus, a comprehensive evaluation across techniques and modalities is necessary.
OBJECTIVE: This systematic review integrates the three state-of-the-art leading approaches, GANs, Diffusion Models, and Transformers, examining their applicability, methodologies, and clinical implications in improving medical image quality.
METHODS: Using the PRISMA framework, 63 studies from 989 were selected via Google Scholar and PubMed, focusing on GANs, Transformers, and Diffusion Models. Articles from ACM, IEEE Xplore, and Springer were analyzed.
RESULTS: Generative AI techniques show promise in improving image resolution, reducing noise, and enhancing fidelity. GANs generate high-quality images, Transformers utilize global context, and Diffusion Models are effective in denoising and reconstruction. Challenges include high computational costs, limited dataset diversity, and issues with generalizability, with a focus on quantitative metrics over clinical applicability.
CONCLUSION: This review highlights the transformative impact of GANs, Transformers, and Diffusion Models in advancing medical imaging. Future research must address computational and generalization challenges, emphasize open science, and validate these techniques in…
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