(untitled) (International journal of artificial intelligence, 2026)
Osteoporosis presents significant challenges in orthopedic dental rehabilitation due to reduced bone mineral density (BMD), altered bone microarchitecture, and impaired healing capacity. Recent advances in digital health technologies, including artificial intelligence (AI), cone-beam computed tomography (CBCT), and clinical data integration systems, offer new opportunities for personalized treatment planning. This study proposes a hybrid system integrating a decision-support software platform with a structured clinical database to improve diagnostic accuracy, optimize prosthetic selection, and predict treatment outcomes in osteoporotic patients. A comprehensive literature review and conceptual system design are presented. Evidence suggests that while osteoporosis alone does not significantly reduce implant survival, individualized assessment of bone quality and systemic factors is essential. The integration of AI-driven analytics and CBCT-based diagnostics into unified digital systems may significantly enhance clinical outcomes and support precision dentistry.