Artificial Intelligence for Bone: Theory, Methods, and Applications

Abstract

Artificial intelligence (AI), a transformative technology rooted in decades of computational evolution, from early symbolic reasoning to modern deep learning breakthroughs, is bringing great impact and opportunities to scientific research. Synchronously, AI is rapidly transforming traditional approaches in bone research. The surge in orthopedic big data, advancements in high‐performance computing, as well as innovative AI algorithms, has led to an explosive growth in applications across bone fundamental research and orthopedic clinical practice. These applications span pathological investigation, biomarker discovery, screening of critical intervention targets, drug discovery, disease diagnosis, treatment assistance, postoperative rehabilitation, and prediction of disease recurrence, highlighting vast potential of AI to facilitate bone research. However, challenges persist regarding data quality, data sharing, interpretability, and ethical considerations. In the future, advances in AI are expected to drive significant progress in drug target identification and new drug discovery, moving orthopedic clinical practice from symptom management toward precision medicine. Additionally, the integration of large language models and parallel intelligence in orthopedics could bring revolutionary changes. We hope this review can provide basis for AI applications in biological research and clinical translation, advancing intelligent and personalized management of musculoskeletal system diseases.