Artificial Intelligence for Evidence Synthesis of Emerging Biologics to Improve Skeletal Health in Osteogenesis Imperfecta: Systematic Review and Meta-Analysis.

Background: Osteogenesis imperfecta (OI) is a rare genetic disorder characterized by bone fragility and recurrent fractures. Emerging biologics demonstrate promise by targeting bone-remodeling pathways, yet evidence for their efficacy and safety remains fragmented and heterogeneous, and no prior systematic review in OI has incorporated artificial intelligence (AI) to synthesize it. Objective: This study aims to systematically evaluate the efficacy and safety of novel biologics in patients with OI using an AI-assisted workflow for evidence synthesis. Methods: We conducted a systematic review and meta-analysis of interventional trials of denosumab, setrusumab, teriparatide, romosozumab, and fresolimumab. Data were retrieved from PubMed, Web of Science, Embase, ScienceDirect, the Cochrane Library, and ClinicalTrials.gov up to December 1, 2025. Eligible studies enrolled individuals with OI, reported areal bone mineral density (aBMD) and/or fractures, and were randomized, nonrandomized, or single-arm studies; case series were excluded. As a methodological feature, GPT-4o was integrated into the workflow to perform a parallel 2-stage screening (title/abstract and full text) and to assist with risk of bias assessment using an adapted Cochrane RoB 2 tool. The primary outcome, percentage change in aBMD, was synthesized using a random-effects meta-analysis. GPT-4o was benchmarked against human reviewers using sensitivity, specificity, and weighted Cohen κ. Results: Thirteen trials (n=684) were systematically reviewed, of which 10 (n=333) contributed to meta-analyses. In children, denosumab produced the greatest 12-month increase in lumbar spine aBMD (25.49%, 95% CI 17.14%-33.84%). In adults, setrusumab at 12 months yielded the highest improvement (9.38%, 95% CI 6.5%-12.26%). Across trials, no biologic significantly reduced fracture incidence compared to bisphosphonates. Safety profiles varied: denosumab was associated with a high risk of hypercalcemia in children (30.95%), whereas setrusumab had no treatment-related serious adverse events. AI achieved high sensitivity in abstract (97.4%) and full-text (88.9%) screening, and reduced total screening time by over 95%. Although there was substantial agreement with humans in the quality assessment (Cohen κ=0.778, 95% CI 0.710-0.846), the model exhibited optimism and positional biases due to reliance on probabilistic language patterns rather than structured clinical reasoning. Conclusions: This review is the first to synthesize and quantitatively compare skeletal outcomes across multiple biologics in OI with an AI-assisted review workflow. Denosumab and setrusumab demonstrate promising efficacy in improving lumbar spine aBMD across ages, although current evidence does not support superior fracture reduction over bisphosphonates. GPT-4o can substantially accelerate evidence synthesis but should be deployed with explicit human oversight in tasks requiring contextual understanding and clinical reasoning. These findings should be interpreted cautiously given the small and heterogeneous trial base. Taken together, our workflow presented how evidence synthesis may be scaled and operationalized in real-world rare disease research.