A Comprehensive Review of Generative AI: Applications, Challenges, and Future Directions

Abstract

Generative AI has revolutionized content creation by enabling machines to produce text, images, music, and even synthetic data. This review paper explores the evolution of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models such as GPT and DALL·E. We analyze their applications in creative industries, healthcare, and scientific research, highlighting their potential and limitations. The paper also discusses key challenges, including ethical concerns, biases, and risks of deepfake technology. Finally, we present future research directions for improving generative AI's robustness, interpretability, and security.