Automating methodological quality assessment in orthopedic systematic reviews using large language models.

Systematic reviews form the bedrock of evidence-based orthopedic practice, yet maintaining consistent quality assessment remains a significant challenge. This critical evaluation step is often labor-intensive, time-consuming, and susceptible to human bias. No prior work had resolved the potential for artificial intelligence to streamline these complex evidence synthesis tasks. Recent developments in large language models offer promising avenues for automating portions of this workflow. That uncertainty drove researchers to investigate whether these computational tools could match human performance. Prior research has shown that manual appraisal processes frequently suffer from inter-rater variability. This gap motivated a formal comparison between automated systems and established expert benchmarks. The field currently lacks a standardized approach for integrating these emerging technologies into routine review procedures.

This study examined whether large language models can perform methodological quality assessment evaluations in orthopedic systematic reviews with accuracy comparable to human experts. The researchers aimed to determine if automated systems could reliably replicate human ratings based on the AMSTAR-1 framework. This investigation addressed the significant time and labor constraints inherent in traditional evidence synthesis processes. By comparing model outputs to expert benchmarks, the team sought to identify which specific appraisal domains are suitable for automation. The study also explored the potential for these tools to improve the overall efficiency of systematic review workflows. Investigators were motivated by the need to reduce subjectivity and enhance the consistency of quality assessments. No prior work had systematically validated these models against established umbrella review data in this clinical specialty. The primary goal was to define the current boundaries of machine-assisted evidence appraisal in the orthopedic field.

Review Approach involved analyzing ten sports medicine knee reviews to evaluate automated assessment performance. Investigators utilized three distinct computational platforms to generate binary ratings for each study. These automated outputs were then compared against established human expert scores from a prior umbrella review. The team compiled 110 total decisions to determine the level of concordance between human and machine. To ensure robust findings, the researchers incorporated an external validation set consisting of four recent publications. This secondary cohort spanned papers released between 2022 and 2025 to test model generalizability. The design specifically aimed to prevent information leakage by using data not present in the primary training set. Experts verified all manual ratings to provide a reliable baseline for measuring model accuracy.

Key Findings From the Literature indicate that agreement with human reviewers reached 87% for GPT-4o, 89% for GPT-5, and 90% for GPT Consensus. All models maintained a consistent 84% agreement rate when tested against the external validation set. Concordance proved strongest for structured domains, specifically a priori design, literature search, and study characteristics. Conversely, the models exhibited the lowest agreement for judgment-based items. These difficult categories included grey literature inclusion, publication bias, and conflict of interest assessments. The data demonstrate that automated systems perform reliably on explicitly reported information. However, the results highlight a clear performance gap when models attempt to interpret subjective research components. Overall, the findings quantify the current capabilities and limitations of artificial intelligence in methodological appraisal.

Synthesis and Implications suggest that artificial intelligence cannot currently substitute for human reviewers in systematic review workflows. These models function as reliable adjunct tools that may enhance overall efficiency and transparency. Researchers observed that automated systems provide reproducible results when applied to structured data domains. The authors propose that these technologies assist in streamlining labor-intensive appraisal tasks within orthopedic research. Future implementation requires careful oversight to address limitations in judgment-based criteria. The findings indicate that human expertise remains necessary for evaluating complex items like publication bias. Systematic review teams might consider these models to support, rather than replace, traditional appraisal methods. This study provides a framework for integrating computational assistance into evidence synthesis practices.