Large language models for risk-of-bias assessment in randomised clinical trials—a comparative validation study

Abstract

SummaryBackgroundLarge language models (LLMs) are emerging tools for evidence synthesis. Risk of bias (RoB) assessment of trials remains an essential but time-consuming step inconsistent even amongst experts. Early LLM studies showed mixed reliability. Advances in reasoning-enabled models warrant evaluation of their accuracy and consistency for RoB screening across randomised trials to reduce reviewer workload.MethodsWe conducted a preregistered comparative validation study (March 11-May 19, 2025) of four LLMs—ChatGPT o3, DeepSeek v3, Google Gemini Flash 2.0, and Grok 3—prompted with full-text randomised clinical trial articles and protocols. Two corpora were analysed: 100 RCTs from recent Cochrane reviews (RoB 1) and 100 RCTs from meta-analyses in high-impact journals (RoB 2). The reference standard was published human RoB judgements. The primary outcome was interobserver reliability (Cohen κ, 95% CI); secondary outcomes were intraobserver agreement and diagnostic accuracy (sensitivity, specificity, predictive values, F1-score).FindingsFor RoB 1, interobserver agreement ranged from κ 0.0.27 (95% CI 0.07-0.46) with Gemini Flash 2.0 to κ 0.39 (0.20-0.59) with DeepSeek v3. For RoB 2, agreement was lower, from κ 0.06 (−0.07 to 0.18) with ChatGPT o3 to κ 0.13 (−0.04 to 0.31) with Gemini. Diagnostic performance was limited with sensitivity ranging 0.05-0.55, specificity 0.78-0.99, PPV 0.31-0.50, and NPV 0.48-0.61 across models, with models consistently over-flagging concerns.InterpretationNone of the evaluated LLMs were sufficiently reliable for fully autonomous RoB assessment. DeepSeek v3 and ChatGPT o3 approximated human performance best on RoB 1, but RoB 2 rule-in and rule-out performance remained modest. Current use should be supervised, with possible application of LLMs for triage or as a second assessor. Major improvements in protocol retrieval, task-specific tuning, and calibrated thresholds, prospectively validated, are needed for safe stand-alone deployment.FundingThis study received no financial support.