ArticleDigital health
Custom GPT models for complex rheumatology systematic reviews: A two-part evaluation of data extraction and prognosis appraisal.
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9 authors.
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Abstract
Background: Systematic reviews are essential for evidence-based practice but remain resource-intensive, particularly during full-text data extraction and structured risk-of-bias appraisal in prognostic research. These challenges are amplified in complex autoimmune diseases such as systemic lupus erythematosus (SLE). Recent advances in large language models (LLMs) have raised interest in their potential; however, rigorous benchmarking against expert reviewers in real-world rheumatology settings is limited. Objective: To evaluate the feasibility, agreement, and efficiency of customized GPT-based LLMs across two systematic-review tasks: 1) study-level data extraction in metabolomics studies of SLE, and 2) prognostic risk-of-bias appraisal in rheumatology studies using QUIPS. Methods: This two-part methodological study was nested within two PROSPERO-registered reviews. For data extraction, fifteen full-text SLE metabolomics studies were processed by human reviewers and by a customized GPT model using a shared, structured template; concordance across predefined fields and extraction time per study were compared. For prognostic appraisal, nineteen rheumatology prognostic studies with adjudicated human QUIPS domain ratings (Low/Moderate/High) were reappraised in 2025 using a customized ChatGPT model (GPT-Reviewer). Agreement with the human reference was quantified using weighted kappa (quadratic weights) with 95% confidence intervals. Results: GPT-Reviewer generated complete domain-level QUIPS judgments for all 19 studies, with heterogeneous concordance versus adjudicated human ratings. Domain-specific κw was 0.001 for study participation (95% CI 0.000-0.002) and outcome measurement, 0.129 for study attrition (95% CI 0.028-0.241), 0.137 for prognostic factor measurement (95% CI 0.000-0.478), 0.286 for statistical analysis/reporting (95% CI 0.161-0.322), and 0.681 for study confounding (95% CI 0.488-0.847). The mean extraction time was shorter for the GPT model than for human reviewers (5.7 vs. 30.4 minutes per study). Conclusions: Customized GPT-based LLMs are best deployed as complementary tools within human-in-the-loop workflows; improved handling of tables/supplements and domain-specific calibration are needed before routine use in complex rheumatology evidence synthesis.
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