This review article explores the application of artificial intelligence (AI), particularly deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in fracture detection and classification. The authors highlight the challenges in radiological interpretation, including human error, fatigue, and time constraints, which can lead to missed or misdiagnosed fractures. AI, especially deep learning, offers significant advantages in terms of accuracy, speed, and efficiency in fracture detection. CNNs are particularly effective in analyzing large datasets and identifying intricate patterns, while RNNs excel in handling sequential data and temporal information. The integration of these models with attention mechanisms and transfer learning further enhances their performance. The review also discusses the use of natural language processing (NLP) to extract relevant information from medical records, which can support radiologists in making more informed decisions. Additionally, the article examines the potential of combining AI with other imaging modalities like CT and MRI to improve fracture diagnosis and treatment planning. While AI shows promise, it also raises ethical and legal concerns, such as accountability and the need for robust data labeling. Overall, the authors conclude that AI can significantly enhance the accuracy and efficiency of fracture detection, but further research is needed to address its limitations and ensure its safe and effective implementation.This review article explores the application of artificial intelligence (AI), particularly deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in fracture detection and classification. The authors highlight the challenges in radiological interpretation, including human error, fatigue, and time constraints, which can lead to missed or misdiagnosed fractures. AI, especially deep learning, offers significant advantages in terms of accuracy, speed, and efficiency in fracture detection. CNNs are particularly effective in analyzing large datasets and identifying intricate patterns, while RNNs excel in handling sequential data and temporal information. The integration of these models with attention mechanisms and transfer learning further enhances their performance. The review also discusses the use of natural language processing (NLP) to extract relevant information from medical records, which can support radiologists in making more informed decisions. Additionally, the article examines the potential of combining AI with other imaging modalities like CT and MRI to improve fracture diagnosis and treatment planning. While AI shows promise, it also raises ethical and legal concerns, such as accountability and the need for robust data labeling. Overall, the authors conclude that AI can significantly enhance the accuracy and efficiency of fracture detection, but further research is needed to address its limitations and ensure its safe and effective implementation.