Artificial Intelligence in Musculoskeletal Oncology

The field of oncological orthopedics has undergone a transformative shift with the integration of artificial intelligence (AI) technologies. AI applications in primary bone and soft-tissue cancer, metastatic disease, and other aspects of oncological orthopedics have grown significantly over the past two decades. While there has been a substantial increase in research papers and interest in the subject, practical clinical applications of AI in this field still need to be improved. Radiology, a crucial component in diagnosing musculoskeletal tumors, has witnessed the implementation of AI-powered algorithms for detecting and differentiating benign and malignant lesions in various imaging modalities. Histopathology, essential in diagnosis and treatment decisions, is enhanced by AI, which accelerates tumor classification through pattern recognition. AI’s integration in molecular biology enables personalized treatment strategies based on genomic and proteomic data analysis. Beyond radiology, histology, and molecular biology, AI’s applications extend to clinical outcomes and research. Despite these prospects, AI in orthopedic oncology faces challenges like overfitting, interpretability, and data limitations. The future holds promise as techniques like transfer learning and ensemble models evolve and AI becomes integrated into clinical practice for non-interpretive tasks. Over time, AI’s potential to offer precise diagnoses, personalized treatments, and improved patient outcomes could revolutionize the landscape of orthopedic oncology.