Artificial Intelligence‐Driven Precision Bisphosphonate Therapy in Osteoporosis: Mechanisms, Clinical Applications, Risks, and Future Directions
- 1The Affiliated People's Hospital of Jiangsu University Zhenjiang Jiangsu Province China
- 2Jiangsu University Zhenjiang Jiangsu Province China
- 3Taizhou First People's Hospital Taizhou Zhejiang Province China
Abstract
Bisphosphonates remain the pharmacological standard of care for osteoporosis; however, their clinical efficacy is limited by significant diagnostic gaps, heterogeneous therapeutic responses, and persistent concerns regarding long‐term safety, including medication‐related osteonecrosis of the jaw (MRONJ) and atypical femoral fractures (AFF). To address these limitations, a structured comprehensive search of the Web of Science Core Collection, Scopus, and PubMed (January 2005–December 2025) was conducted to evaluate the intersection of bisphosphonate pharmacotherapy and computational medicine. The integration of artificial intelligence (AI) facilitates a methodological transition from empirical prescription to precision medicine across critical clinical domains. (1) Screening and Diagnosis: AI‐driven opportunistic screening utilizes routine computed tomography (CT) scans to automate asymptomatic vertebral fracture detection and assess bone quality via radiomics. (2) Treatment Stratification: Machine learning algorithms enable the prediction of individual bone mineral density (BMD) responses and optimize alternative or sequential therapies, facilitating precise patient selection for potent agents like zoledronic acid. (3) Safety Monitoring: Predictive models integrating clinical and imaging data allow for the pre‐emptive risk stratification of adverse events, shifting management from passive surveillance to active prevention. (4) Smart Delivery Systems: Furthermore, computational modeling accelerates the design of stimuli‐responsive nanocarriers to improve bone targeting and reduce systemic toxicity. Despite current challenges regarding algorithmic interpretability and multi‐center validation, incorporating emerging technologies—such as large language models (LLMs) for clinical triage and the integration of systemic metabolic networks (e.g., the gut‐bone axis)—suggests a promising trend toward a data‐informed, risk‐adjusted, and biologically targeted precision ecosystem in osteoporosis management.
Related
- Artificial intelligence-enabled mental health applications for adolescents: a scoping review of safety, effectiveness and clinical implementation
- Artificial Intelligence Applications in Laser-Assisted Refractive Surgery—A Comprehensive Review
- From underdiagnosis to scalable screening: Applications of artificial intelligence for vertebral compression fracture detection – a systematic review and meta-analysis of diagnostic performance
- Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions
- Artificial Intelligence in Emergency Radiology: Current Applications, Challenges, and Future Directions.
BibTeX
@article{yao2026artificial,
title = {{Artificial Intelligence‐Driven Precision Bisphosphonate Therapy in Osteoporosis: Mechanisms, Clinical Applications, Risks, and Future Directions}},
author = {Xiang Yao and Yicong Chao and Xiao Gu and Jingtao Peng and Jishan Yuan and Wei Shi and Fuguo Zhang and Qiwen Bao},
journal = {Orthopaedic Surgery},
year = {2026},
month = sep,
doi = {10.1111/os.70429},
url = {https://doi.org/10.1111/os.70429},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence‐Driven Precision Bisphosphonate Therapy in Osteoporosis: Mechanisms, Clinical Applications, Risks, and Future Directions
AU - Xiang Yao
AU - Yicong Chao
AU - Xiao Gu
AU - Jingtao Peng
AU - Jishan Yuan
AU - Wei Shi
AU - Fuguo Zhang
AU - Qiwen Bao
PY - 2026
DA - 2026/09/17
JO - Orthopaedic Surgery
DO - 10.1111/os.70429
UR - https://doi.org/10.1111/os.70429
AB - Bisphosphonates remain the pharmacological standard of care for osteoporosis; however, their clinical efficacy is limited by significant diagnostic gaps, heterogeneous therapeutic responses, and persistent concerns regarding long‐term safety, including medication‐related osteonecrosis of the jaw (MRONJ) and atypical femoral fractures (AFF). To address these limitations, a structured comprehensive search of the Web of Science Core Collection, Scopus, and PubMed (January 2005–December 2025) was conducted to evaluate the intersection of bisphosphonate pharmacotherapy and computational medicine. The integration of artificial intelligence (AI) facilitates a methodological transition from empirical prescription to precision medicine across critical clinical domains. (1) Screening and Diagnosis: AI‐driven opportunistic screening utilizes routine computed tomography (CT) scans to automate asymptomatic vertebral fracture detection and assess bone quality via radiomics. (2) Treatment Stratification: Machine learning algorithms enable the prediction of individual bone mineral density (BMD) responses and optimize alternative or sequential therapies, facilitating precise patient selection for potent agents like zoledronic acid. (3) Safety Monitoring: Predictive models integrating clinical and imaging data allow for the pre‐emptive risk stratification of adverse events, shifting management from passive surveillance to active prevention. (4) Smart Delivery Systems: Furthermore, computational modeling accelerates the design of stimuli‐responsive nanocarriers to improve bone targeting and reduce systemic toxicity. Despite current challenges regarding algorithmic interpretability and multi‐center validation, incorporating emerging technologies—such as large language models (LLMs) for clinical triage and the integration of systemic metabolic networks (e.g., the gut‐bone axis)—suggests a promising trend toward a data‐informed, risk‐adjusted, and biologically targeted precision ecosystem in osteoporosis management.
SN - 1757-7853
SN - 1757-7861
ER -