Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives

Osteoporosis (OP) is a chronic systemic skeletal disorder that predominantly affects the elderly. It is characterized by an imbalance in bone homeostasis, reduced bone mass, microarchitectural deterioration of bone tissue, and increased bone fragility, ultimately leading to a higher risk of fractures and related complications. With the progression of global population aging, the prevalence of OP continues to rise, underscoring the importance of early diagnosis and timely intervention. However, the diagnosis and management of OP-particularly its early detection-remain limited by material constraints such as diagnostic equipment and by subjective factors including clinician experience, which hinder widespread screening. In recent years, artificial intelligence (AI) has emerged as a transformative technology with advantages of efficiency, objectivity, and scalability, and has been increasingly integrated into various medical domains. For example, AI-assisted musculoskeletal measurements on leg and foot radiographs can reduce the measurement time from 166 seconds to 40 seconds, resulting in an overall efficiency improvement of approximately 70%. Applying AI to the diagnosis and treatment of OP can reduce human error, save labor costs, and improve diagnostic accuracy and clinical efficiency. Numerous studies have investigated AI-based approaches in OP-related research and clinical practice. Despite these promising developments, several important limitations should be acknowledged. Considerable heterogeneity exists among published studies regarding patient populations, AI algorithms, and evaluation metrics. Besides, consistent external validation remains insufficient in many studies. Challenges related to data imbalance and potential selection bias further highlight the need for standardized reporting frameworks and multicenter collaborative research to promote safe clinical adoption of AI technologies in osteoporosis management. This review summarizes current AI applications in OP diagnosis, risk prediction and therapy. We highlight key methodological limitations and emerging trends, aiming to guide future research and facilitate safe clinical implementation of AI in OP management.