Revolutionizing Medical Diagnostics: The Transformative Impact of Generative Adversarial Networks (GANs)

Abstract

Artificial intelligence, notably through Generative Adversarial Networks (GANs), is profoundly transforming healthcare diagnostics by enabling rapid and precise data processing. GANs employ a dual-network architecture consisting of a generator to simulate authentic data and a discriminator to detect forgeries. This innovative mechanism not only generates high-quality images for AI training but also improves the resolution of medical images, enhances diagnostics, reduces human errors, and provides realistic simulations for healthcare training. This review evaluates the recent advancements, addresses the technical and ethical challenges, and explores the future potential of GANs in personalized medicine and their integration with other AI technologies to augment clinical efficacy.