This review explores the advancements in artificial intelligence for radiograph fracture diagnosis, emphasizing technological developments and inherent limitations. Artificial intelligence improves diagnostic accuracy and manages workflow efficiency. The review categorizes artificial intelligence applications in fracture diagnosis into four primary tasks: recognition, classification, detection, and localization. The most popular performance metrics, such as diagnostic accuracy, precision, sensitivity, specificity, and area under the curve analysis, are used to compare artificial intelligence systems with traditional radiological methods and are explained as serving as a guide. Each task and performance metric is illustrated with practical examples and success stories from recent literature, offering insights into the strengths and weaknesses of various artificial intelligence approaches, such as support vector machines, convolutional neural networks, and generative adversarial networks. We also incorporate case analyses, underscoring the potential and limitations of artificial intelligence in fracture detection. In particular, challenges were posed by external factors such as casts and anatomical complexities. Future directions are explored, emphasizing human-artificial intelligence collaboration and the development of more advanced, transparent artificial intelligence systems alongside parallel evolving ethical considerations and regulatory frameworks. This review aims to equip clinicians with the knowledge to understand and utilize artificial intelligence technologies effectively in their practice.  Kulcsszavak: artificial intelligence, machine learning, fracture, trauma, radiography