ReviewOrthopaedic surgery2026
Artificial Intelligence-Driven Precision Bisphosphonate Therapy in Osteoporosis: Mechanisms, Clinical Applications, Risks, and Future Directions.
Review in Orthopaedic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.