To first identify novel biologics under investigation for the treatment of OI, we horizontally scanned ScanMedicine and ClinicalTrials.gov for all phases of clinical trials registered up to December 1, 2025. Trials were excluded if they were withdrawn, suspended, or conducted solely in animal models. Only those reporting preliminary efficacy outcomes were included. Through this process, we identified 7 biologics: teriparatide, fresolimumab, setrusumab, romosozumab, blosozumab, somatropin, and denosumab.

To further evaluate the clinical efficacy of biologics, we systematically searched PubMed, Embase, ScienceDirect, Web of Science, and the Cochrane Library for human studies published up to December 1, 2025. All databases and registries were searched separately, not via a multidatabase platform. No additional trial registries (eg, WHO ICTRP) were searched. The search terms combined free-text in titles, abstracts, and keywords: (“osteogenesis imperfecta” OR “fragilitas ossium” OR “dysostosis” OR “osteopsathyrosis” OR “brittle bone disease”) AND (“teriparatide” OR “fresolimumab” OR “setrusumab” OR “BPS804” OR “romosozumab” OR “blosozumab” OR “TST002” OR “somatropin” OR “denosumab”). Because search syntax differs across interfaces, the full database-specific strategies (including field tags/modifiers) are reported in Table S1 in Multimedia Appendix 1. Additionally, ClinicalTrials.gov was searched for relevant ongoing or completed but unpublished trials by entering “osteogenesis imperfecta” in the Condition or Disease field and the investigational drug name in the Intervention/Treatment field.

We also undertook supplementary browsing of conference proceedings and pharmaceutical press releases to supplement outcome data when original trial results were not available. In addition, we hand-searched the reference lists of included trials for additional eligible articles and contacted trial investigators when necessary to obtain missing information, such as standard deviations. Forward citation searching using citation indexes was not conducted. No published search filters were applied. Searches were run once, with restriction to English-language publications and a last update on December 1, 2025, for all databases; no study-design limits were otherwise imposed. The search strategy was developed de novo, was not adapted from previous reviews, and was formally peer-reviewed by a third information specialist.

Table S1 in Multimedia Appendix 1 also reports the number of records retrieved from each database and the total number of records before and after deduplication in EndNote (Clarivate). All records were imported into EndNote 21 for automated deduplication and then exported to Microsoft Excel for additional manual removal of duplicates; therefore, post-deduplication counts are only available for the overall dataset, not for each individual database. The literature search and their reporting followed the PRISMA-S checklist (Checklist 2) [25].

Two human reviewers independently performed data extraction using a standardized form, and only articles that passed human screening were included. Extracted data included (1) innovative medicine; (2) first author and publication year; (3) study design; (4) follow-up period; (5) types of OI; (6) methods of diagnosis; (7) participants (gender and age); (8) sample size and intervention (per group); (9) primary efficacy outcomes (changes in bone mineral density [BMD], fracture incidence, functional or pain scores, and other prespecified clinical endpoints); and (10) secondary safety outcomes (adverse events, serious adverse events, and mortality). If the required data were missing, attempts were made to contact the authors to request the additional information for clarification.

For drugs with at least two trials, meta-analyses of efficacy outcomes were performed using the meta package in R (version 4.4.3), applying a random-effects model with the Hartung-Knapp-Sidik-Jonkman (HKSJ) method to account for between-study heterogeneity [28]. The primary efficacy outcome was the BMD z score, percentage change in BMD from baseline, and fracture incidence. For the main analyses, we conducted single-arm pre-post meta-analyses by pooling only the innovative-treatment arms from each eligible trial, irrespective of whether the original study was randomized or nonrandomized. Comparative effectiveness, when available, is reported separately in Table S4 in Multimedia Appendix 1 [14,29-39]. Each clinical trial contributed only a single effect estimate to each pooled analysis, thereby avoiding any double-counting of trials.

In accordance with Cochrane guidelines, publication bias was not formally assessed due to the limited number of studies (<10 per analysis), thus precluding the use of the Egger regression or funnel plot asymmetry tests. To address age-related heterogeneity, we first performed a primary meta-analysis at the all-age level, including all eligible trials, then conducted subgroup analyses stratified by age group (adults vs children). Additionally, to examine the impact of original study design, we conducted sensitivity analyses stratified by study design, pooling (1) intervention arms from randomized trials separately from (2) nonrandomized single-arm/self-controlled studies; results are presented in Figure S1 (a-e) in Multimedia Appendix 1. Results for drugs that were not eligible for pooling with fewer than two trials or only qualitative, nonpooled efficacy data are narratively summarized in Section S4a in Multimedia Appendix 1.

The comparison between manual and GPT-based screening is summarized in Figure 1. Both approaches started from the same pool of 594 records, with 294 unique records screened after deduplication. GPT-assisted screening was applied at the report level and classified 52 full-text reports as eligible following title/abstract and full-text assessment, whereas manual screening ultimately included 18 eligible full-text reports from the same pool. Because multiple reports could originate from the same underlying trial, all eligible full-text reports were subsequently mapped to unique studies in a separate, manual study-level consolidation step, in which multiple reports of the same trial were merged. After this consolidation, 13 unique studies were included in the systematic review [14,29-40], of which 10 contributed data to the meta-analysis [29,30,32-34,36-40].

Notably, GPT-based screening dramatically reduced workload, with an average screening time of 3.5 (SD 0.14) seconds per article for title and abstract screening and 9.67 (SD 1.85) seconds for full-text review (Figure S2 in Multimedia Appendix 1), compared to approximately 15 minutes per article for manual screening. This resulted in over 95% total time savings and an approximately 100-fold increase in screening speed per article.

Using manual review as the benchmark, GPT achieved high sensitivity—97.4% for title/abstract screening and 88.9% for full-text review—but lower specificity, at 77% and 87%, respectively, indicating a tendency to favor recall over specificity (Table 1).

A total of 13 trials were included (684 participants) [14,29-40]: 8 controlled studies (4 double-blind trials [29,33,35,39], 2 open-label trials with zoledronate as active comparator [36,37], 1 phase I trial [14], and 1 historical-controlled study [38]), and 5 uncontrolled trials (3 single-arm trials [30,31,34] and 2 open-label, self-controlled trials [32,40]). Interventions included denosumab (n=6), teriparatide (n=3), fresolimumab (n=1), romosozumab (n=1), and setrusumab (n=2), with comparators such as placebo or bisphosphonates. The studies enrolled 355 adults and 329 children and reported outcomes included changes in areal or volumetric BMD, BMD z score, fracture incidence, bone turnover markers, and safety profiles. Study characteristics are presented in Table 2, with efficacy outcomes detailed in Table S2 in Multimedia Appendix 1.

Manual risk of bias in 10 trials is presented in Figure S3 in Multimedia Appendix 1. Four trials [30,33,34,40] were rated as low risk across all domains. Five trials exhibited a high risk of bias [32,36-39], mainly due to subjective assessment of spinal morphometry in outcome measurement (n=4) [32,36-38], or missing values that could have biased the outcome measurements (n=1) [39]. The overall risk of bias was adjudicated as some concerns for all 10 studies.

Table S1 in Multimedia Appendix 1 compares the risk of bias between human evaluators and the GPT-based tool for the 10 studies. Overall, substantial agreement was observed (weighted κ=0.778, 95% CI 0.710‐0.846) with no significant heterogeneity across studies. All studies except Glorieux et al [33] and Mei et al [38] exceeded the 0.6 threshold for substantial agreement. The greatest discrepancies between human and GPT evaluations were found in the domains of randomization, followed by measurement of the outcome.

Twelve trials reported percentage change in lumbar spine aBMD [14,29,31-40], of which 9 trials (180 participants) provided sufficient data for inclusion in the formal meta-analysis (Figure 2) [29,32-34,36-40]. A random-effects model using the HKSJ method was applied, given the clinical and methodological diversity across trials. The pooled percentage increase was 12.93% (95% CI 5.4%‐20.45%). Of the 9 pooled studies, 5 were at high overall risk of bias, mainly due to open-label designs and nonblinded aBMD assessment [32,36-39], potentially introducing subjective measurement influences and additional uncertainty around the pooled effect. Between-study heterogeneity was substantial (I²=88.6%, τ²=37.10). Subgroup analyses by drug showed statistically significant differences between treatments (test for subgroup differences: χ²₂=9.2, P=.01). Denosumab at 12 months was associated with the largest mean increase (18.76%, 95% CI 5.44%‐32.08%; I²=88.7%), whereas teriparatide at 18 months showed a smaller and statistically nonsignificant increase (5.81%, 95% CI −4.66% to 16.27%; I²=0%). Setrusumab at 12 months showed a mean increase of 15.61%, but with a very wide and imprecise confidence interval (95% CI −66.11% to 97.34%; I²=94.3%), indicating considerable uncertainty about the true treatment effect.

Subgroup analysis showed that in children (Figure 3), the pooled percentage increase in lumbar spine aBMD across the two biologics was 23.54% (95% CI 18.99%‐28.08%; I²=0%, τ²=0). Denosumab at 12 months was associated with a mean increase of 25.49% (95% CI 17.14%‐33.84%; I²=0%; τ²=0) and setrusumab at 12 months showed a similar effect (22.25%, 95% CI 16.94%‐27.56%; τ²=0). The difference between the two biologics was not statistically significant (test for subgroup differences: χ²₂=0.7, P=.39).

In adults (Figure 4), the pooled percentage increase in aBMD across the three biologics was 6.1% (95% CI 3.13%‐9.06%; I²=47.2%, τ²=2.53), indicating moderate between-study heterogeneity. There was evidence of differences between agents (test for subgroup differences: χ²₂=14.3, P<.001). Setrusumab at 12 months produced the largest effect (9.38%, 95% CI 6.5%‐12.26%). In contrast, denosumab at 12 months (4.29%, 95% CI 1.27%‐7.31%; I²=0%) and teriparatide at 18 months (5.81%, 95% CI −4.66% to 16.27%) showed smaller and, for teriparatide, statistically uncertain effects. Notably, the setrusumab trial was judged to be at low risk of bias, whereas 4 trials evaluating denosumab and teriparatide were at high risk of bias, adding further uncertainty to these estimates.

Evidence on fracture reduction with biologics in OI remains limited and mixed. Across 3 trials [30,36,37] and 1 interventional study with historical control [38], denosumab demonstrated fracture rates comparable to bisphosphonates (zoledronic acid and alendronate) and in both adults and children. Intensifying the dosing frequency to every 3 months in children did not yield a lower fracture rate (Q3M: 26.7% vs Q6M: 28.3%; P>.05) [30]. For teriparatide, 2 adult trials [35,39] found teriparatide (20 μg/day) reduced fracture risk compared to controls, although results were not statistically significant. New fracture rates were lower with teriparatide versus placebo (29% vs 36%, odds ratio 0.73) over 18 months [39] and intravenous neridronate over 24 months (16.3% vs 26.5%; P=.10) [35]. A single trial with setrusumab showed dose-dependent fracture reduction [33]: the 20 mg/kg group had the lowest fracture incidence (16.1%) and annualized fracture rate (0.19 per participant-year), compared to 34.5% and 0.54 in the 8 mg/kg group over 12 months.

Ten studies reported adverse events as safety outcomes [14,29-34,36-38]. Selective outcomes are summarized in Table 3, while all reported outcomes are listed in Table S5 in Multimedia Appendix 1. Metabolic disturbances were notable, with denosumab associated with a high incidence of hypercalcemia (up to 30.95%) and hypercalcemic crisis (14.29%) in children [37]. In adults, denosumab did not increase the risk of hypercalcemia compared to zoledronic acid and significantly reduced the risk of acute-phase reactions (P=.002) [36]. Teriparatide showed the highest rate of nausea among adults (53.85%) [32], but serious adverse event rates were similar to placebo or neridronate in head-to-head trials [35,39]. Fresolimumab was associated with medication-related adverse events in all 8 patients with type III OI in a single-arm trial, most commonly epistaxis, though none were classified as serious [14]. Setrusumab emerged as a relatively safe biologic, with no cases of hypercalcemia or treatment-related serious adverse events. Reported adverse events in adults included transient hypocalcemia and a mild headache [33]. Phase 2 trial also confirmed acceptable tolerability in young patients [29]. In a phase 1 pediatric trial of romosozumab, 48% of participants experienced mostly mild-to-moderate treatment-emergent adverse events (TEAEs), with two serious TEAEs (femur and lower-limb fractures) deemed unrelated to treatment [31].

This study addressed the clinical challenges of synthesizing fragmented trial evidence in a rare disease—OI. Clinically, among emerging biologic therapies, denosumab and setrusumab could potentially improve aBMD across age groups, but comparative evidence remains limited and is not yet robust enough to demonstrate clear efficacy superiority or fracture benefits over traditional BPs. To streamline the review process, we also implemented an LLM-assisted, automation-enabled, human-in-the-loop workflow for key steps, including screening and risk of bias assessment. Its outputs showed acceptable agreement with consensus human decisions.

Patients with OI face substantial unmet needs, with fracture reduction and improved quality of life as central goals [41]. However, most existing studies use changes in aBMD as a surrogate outcome rather than directly assessing clinically meaningful endpoints such as fracture incidence, mobility, and patient-reported measures [42,43]. This is important because a higher aBMD does not always equate to stronger bones in OI [44]. Against this backdrop, our review is the first to systematically evaluate emerging biologics in OI, synthesizing age-specific advantages while underscoring the shared limitations of relying on surrogate endpoints.

In pediatric populations, both denosumab and setrusumab significantly improved lumbar spine aBMD, with denosumab showing the highest overall gain over 12 months. Nevertheless, the largest RCT has not demonstrated a significant advantage of denosumab over intravenous BPs [37], the current standard of care. For setrusumab, early improvements were observed within 12 months, but long-term efficacy and safety remain unconfirmed, as the COSMIC trial (NCT05768854) [45]—the only ongoing phase 3 RCT directly comparing setrusumab and BPs in children over a 24-month period—remains under recruitment. Evidence for fresolimumab in children is lacking, and teriparatide is contraindicated in children due to the acknowledged risk of osteosarcoma [46]. These findings suggest that, while biologics show potential in children, they are not validated for routine use and do not surpass established therapies.

In adults, setrusumab exhibited the greatest increase in lumbar spine aBMD, with higher doses yielding greater gains. This dose-response relationship may be explained by the mechanism proposed by Hosseinitabatabaei et al [47], whereby higher doses stimulate modeling-based bone formation by increasing bone mass directly without requiring prior resorption. This suggests that higher dosing may benefit patients requiring rapid bone accrual. Response to anabolic agents appears subtype-dependent: teriparatide showed efficacy mainly in type I OI [39], with limited benefit in types III and IV, while fresolimumab was effective only in type IV [14], and associated with decreased aBMD in types III and VIII. This means in the adult population, biologics should be considered primarily in the context of clinical trials, with treatment tailored to patient-specific risk profiles and OI subtypes.

Despite aBMD gains with denosumab and setrusumab across age groups, evidence for fracture risk reduction is limited. Denosumab has not shown a significant lower risk in fracture rates versus BPs, even with intensified dosing, which contrasts with findings in osteoporosis [48,49]. This disparity reflects fundamental differences in pathophysiology: in osteoporosis, increased osteoclast activity drives fragility, while in OI, it stems from genetic mutations impairing collagen synthesis. As El-Gazzar et al [50] noted, denosumab reduces bone resorption but does not improve collagen production, folding, or mineralization. Consequently, mineralized material accumulates unevenly within an abnormal collagen matrix, leading to an “over-mineralized but fragile” matrix. Similarly, Rummler et al [51] observed that setrusumab increased bone mass in OI but failed to alter the bone’s multiscale structure, suggesting that fragility arises from other unexplored aspects of bone organization at higher length scales. Also, evidence directly linking specific aBMD changes to fracture risk in OI remains inconclusive. A recent prospective OI cohort did find that lower baseline aBMD (z score < −2 SD) predicts a higher fracture risk, but genotype (COL1 splicing/stop/frameshift variants) exerted an even stronger effect, indicating that bone quality and collagen defects may modify the aBMD-fracture relationship [52].

These findings highlight the limitations of aBMD as a surrogate endpoint in OI. Clinically, what matters most to patients is whether biologics reduce fracture rates, improve mobility, and alleviate pain. Future RCTs should therefore prioritize comprehensive bone quality markers—such as the trabecular bone score and microstructural imaging—alongside direct measures of fracture reduction and patient-centered outcomes.

This study has several limitations. First, the limited number of eligible trials, particularly for romosozumab and fresolimumab, precluded pooled analyses and publication bias assessments. Second, the short duration of included studies (≤24 months) limits insights into long-term efficacy, safety, and rebound effects, which is crucial given OI typically necessitates lifelong pharmacological treatment. Third, clinical heterogeneity from variations in OI subtypes, doses, and prior BP use, together with substantial design heterogeneity and limited comparative data, complicates quantitative cross-trial comparisons and precludes a formal certainty-of-evidence assessment (eg, GRADE). Fourth, all prompts were administered to models without prior domain-specific fine-tuning; therefore, our findings may not reflect the full potential of LLMs further trained or adapted specifically for systematic review tasks. Finally, our search was restricted to English-language publications, which may have introduced language bias and led to the omission of relevant non-English studies. In addition, we relied solely on free-text terms in titles, abstracts, and keywords, and did not use controlled vocabulary (eg, MeSH/Emtree), which may have reduced the sensitivity of the search and resulted in missed studies. However, reference lists of included trials were also hand-searched for additional eligible studies, which may have partially mitigated the risk of missing relevant evidence. Given that this review provides some of the first evidence on the use of AI in a rare genetic disorder with a relatively small evidence base, future research should replicate similar AI-assisted workflows in scientific fields with larger and more complex datasets to determine how well AI performs in reviews involving larger evidence bases.

This review is distinctive in that it both synthesizes the fragmented trial evidence on emerging biologics in OI and prospectively evaluates an LLM-assisted workflow that combines screening and risk-of-bias assessment against consensus human judgments. Unlike prior reviews that focus either on pharmacologic efficacy or AI methods, we provide a unified, quantitative evaluation of both clinical effects and AI performance within a real-world rare-disease review. Taken together, our findings suggest that biologics should remain largely confined to clinical trials until fracture and safety outcomes are better established, while LLM-based tools can be used to reduce reviewer workload under structured human oversight and to inform the integration of LLM-assisted evidence surveillance into “living” rare-disease guidelines (eg, automated literature monitoring followed by targeted human appraisal). At a policy level, these findings highlight the need for governance frameworks that define minimum performance standards, transparency requirements, and human-oversight safeguards for AI-driven updates to rare-disease clinical guidance.

In conclusion, denosumab and setrusumab show potential efficacy in improving aBMD across age groups in patients with common COL1-related OI types (I-IV), yet evidence for fracture reduction remains inconclusive. Integrating biologics into clinical practice will require more rigorous, long-term RCTs, especially in children. LLM-based tools can improve evidence synthesis in rare diseases by integrating fragmented and heterogeneous clinical trial data. Refined prompts that support context-aware reasoning are essential for trustworthy AI, enabling LLMs to automate rule-based tasks under instruction-based workflows.