A Comprehensive Review on AI-Enhanced Medical Image Generation Methods

Abstract

This extensive examination delves into the dynamic field of AI-driven medical image generation, highlighting the diverse applications of various Generative Adversarial Networks (GANs). As artificial intelligence increasingly integrates into the healthcare sector, the synthesis of artificial medical images has emerged as a pivotal area of study, offering significant prospects for enhanced diagnostics, training, and data augmentation. This burgeoning field presents its own set of challenges, including the necessity for high fidelity, diversity, and interpretability in the generated images. The study involves a comprehensive analysis and comparison of different GAN architectures employed in medical image generation, exploring their individual strengths and limitations and providing a nuanced understanding of their capabilities and constraints. Additionally, the review elucidates the distinctive challenges posed by medical image synthesis, such as the need for images that accurately represent complex medical conditions while maintaining high quality and clinical relevance. It suggests avenues for refinement, such as improving training datasets and developing more sophisticated GAN models to enhance the quality and applicability of generated images. By offering a clearer picture of the status, progress, and future trajectories of AI-powered medical image generation, this review aspires to contribute to the broader discussion on the convergence of artificial intelligence and healthcare, underscoring the potential of GANs to revolutionize medical imaging while acknowledging the technical and ethical considerations that must be addressed to fully realize this potential.