(untitled) (International Journal of Environmental Sciences, 2025)
One of the most important and common diagnostic procedures in clinical radiology is fracture identification. Skeletal fractures can now be automatically detected and classified more quickly and accurately with the integration of Artificial Intelligence (AI) technologies, particularly deep learning (DL) methods like Convolutional Neural Networks (CNNs). Due to the evolution in the machine learning it creates a necessity of potential and appealing fracture prediction and classification system. This paper offers a thorough investigation of current AI-based techniques for detecting fractures in a variety of skeletal radiographs. The study provides insight into the future direction of AI-assisted diagnostic imaging while emphasizing clinical relevance by showcasing recent deployments and real-world use scenarios. The accuracy of the classifier has the utmost importance. The more the accurate classifier the more reliable it is. There are several shallow and deep classifiers available for classification or prediction. The effectiveness and efficiency of the classifier is the major parameter that decides the reliability of the classifier. The study highlights the effectiveness of CNNs, ensemble models, and hybrid GAN-CNN architectures in improving classification accuracy, sensitivity, and specificity. While AI shows promise, challenges remain, including limited validation across diverse demographics, imaging modalities, and complex fracture types. Comparative analyses reveal that models like GAN-CNN achieve higher accuracy, whereas region-specific models excel in localized tasks. Despite AI’s ability to enhance diagnostic workflows and reduce radiologists’ workload, its performance often falls short of human experts in complex scenarios. Clinical deployment demands improvements in cross-center validation, interpretability, and dataset standardization to ensure reliability. Continued research and integration into clinical systems are essential for AI to support timely and precise fracture diagnosis, especially in resource-limited settings.