Artificial intelligence (AI) has gained significant attention in the field of X-ray imaging, offering the potential to enhance various aspects of image-guided interventions. This paper explores the applications of AI algorithms in Xray imaging, focusing on their impact on diagnostic accuracy, workflow optimization, and image quality improvement. Convolutional neural networks (CNNs) have demonstrated remarkable performance in detecting fractures, lung nodules, and other pathologies in X-ray images (Litjens et al., 2017). Computer-aided diagnosis (CAD) systems powered by AI assist radiologists in image analysis by automatically flagging potential abnormalities or regions of interest (Shen et al., 2019). AI algorithms further optimize workflow by prioritizing critical cases and automating repetitive tasks (Chartrand et al., 2017). Additionally, AI techniques such as generative adversarial networks (GANs) enhance image quality and reduce radiation dose by generating synthetic images and performing denoising (Wolterink et al., 2017). However, challenges related to training data, interpretability, and integration into clinical practice need to be addressed. Collaborative efforts among healthcare institutions and regulatory bodies are necessary to develop standardized datasets, establish transparent decision-making processes, and ensure the safe and responsible use of AI in X-ray imaging.