Paper Title
THE ROLE OF AI IN BONE CANCER DETECTION AND CLASSIFICATION: A REVIEW OF CURRENT METHODS AND FUTURE DIRECTIONS

Abstract
Bone cancer is a critical health concern characterized by high mortality and complex treatment challenges, often due to late diagnosis. Early and accurate detection is essential for improving patient outcomes, but traditional diagnostic methods such as X-rays, MRI, and CT scans depend heavily on expert interpretation and are often time-consuming and variable. This paper is use to provides a comprehensive review of recent advancements in artificial intelligence (AI) technologies for the identification and classification of bone cancer, highlighting their potential to address these challenges.AI, with its capacity for analyzing complex medical imaging data, offers significant potential to enhance diagnostic accuracy and speed. The review investigates a capability of recent techniques, including neural networks, machine learning, deep learning and assesses their effectiveness in distinguishing between infected bone and healthy bone tissues. It highlights the advantages of these AI systems in improving efficiency and accuracy, as well as their potential to standardize diagnostic practices. Additionally, it addresses the current limitations and challenges in deploying AI in clinical settings, such as data quality, model interpretability, and integration with existing medical workflows. The findings underscore the promise of AI in revolutionizing bone cancer diagnosis and emphasize the need for continued research to refine these technologies for clinical application. Keywords - Bone cancer, Diagnostic, Machine learning, Deep learning, Neural networks, Medicalimaging, Diagnosticaccuracy, Clinicalapplications, Model interpretability.