Artificial intelligence in bone biology: Transforming basic research into advanced therapeutics

Bone biology orchestrates complex interactions at cellular, molecular, and systemic levels, maintaining skeletal homeostasis and systemic physiology. While significant progress has been made in understanding bone pathophysiology, traditional research methods face challenges in deconvoluting the intricate bone microenvironment and translating findings into effective therapeutic strategies. Artificial intelligence (AI) directly addresses these limitations with its exceptional capacity for analyzing vast, complex datasets and identifying subtle patterns that are often missed by conventional approaches. This review explores how AI is revolutionizing bone biology research and accelerating its translation into therapeutic applications. We categorize AI techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), and systematically assess their applications in bone biology. AI shows remarkable potential in decoding complex cellular signaling networks, identifying novel biomarkers, enhancing diagnostic accuracy, and enabling personalized therapeutic approaches for bone disorders. We critically evaluate current implementations and delineate challenges related to data quality, model interpretability, and ethical considerations. Finally, we propose future directions focusing on data standardization, multi-omics integration, and cross-disciplinary collaboration to unlock the full potential of AI in advancing bone health management and developing novel therapies.