Artificial intelligence (AI) is increasingly being used in fracture detection, offering potential benefits in improving diagnostic accuracy and efficiency. This review discusses the application of deep learning techniques, particularly convolutional neural networks (CNNs), in orthopedic trauma imaging. CNNs have shown the ability to detect and classify fractures with performance comparable to expert radiologists. The integration of AI with various imaging modalities, such as CT and MRI, has also shown promise in enhancing fracture detection and characterization. Recurrent neural networks (RNNs), including long short-term memory (LSTM) networks, are being used to analyze sequential data for fracture identification. Natural language processing (NLP) is also being applied to extract relevant information from medical records and radiological notes, aiding in fracture detection and diagnosis. While AI offers significant advantages in improving diagnostic accuracy and efficiency, there are also challenges, including ethical and legal concerns, data accuracy, and the need for further research to develop robust AI models. The review highlights the potential of AI in fracture detection and classification, while emphasizing the importance of addressing technical and ethical challenges to ensure its effective integration into clinical practice.Artificial intelligence (AI) is increasingly being used in fracture detection, offering potential benefits in improving diagnostic accuracy and efficiency. This review discusses the application of deep learning techniques, particularly convolutional neural networks (CNNs), in orthopedic trauma imaging. CNNs have shown the ability to detect and classify fractures with performance comparable to expert radiologists. The integration of AI with various imaging modalities, such as CT and MRI, has also shown promise in enhancing fracture detection and characterization. Recurrent neural networks (RNNs), including long short-term memory (LSTM) networks, are being used to analyze sequential data for fracture identification. Natural language processing (NLP) is also being applied to extract relevant information from medical records and radiological notes, aiding in fracture detection and diagnosis. While AI offers significant advantages in improving diagnostic accuracy and efficiency, there are also challenges, including ethical and legal concerns, data accuracy, and the need for further research to develop robust AI models. The review highlights the potential of AI in fracture detection and classification, while emphasizing the importance of addressing technical and ethical challenges to ensure its effective integration into clinical practice.