Assessing the risk of bias of clinical trials with large language models and ROBUST-RCT: a feasibility study

Abstract Risk of bias assessment is a crucial step in evidence synthesis. The traditionally adopted tool, however, is complex, resource-intensive, and unreliable. While prior investigations have focused on whether Large Language Models (LLMs) could perform assessments with RoB 2, this study is the first to evaluate the reliability of ROBUST-RCT, a novel risk-of-bias tool, as applied by humans and LLMs. Reviewers working independently used ROBUST-RCT to assess different aspects of a sample of RCTs and then reached a consensus through discussion. A chain-of-thought prompt instructed four LLMs on how to apply ROBUST-RCT. The primary analysis used Gwet’s AC2 to assess inter-rater reliability based on all the final ratings (i.e., the ratings in the second step of the tool) for all the core items of the ROBUST-RCT. A sample of 56 assessments, derived from 9 studies, was compared for each LLM against human consensus. In the primary analysis, Gwet’s AC2 inter-rater reliability varied across the LLMs. DeepSeek-R1, the lowest performer, yielded an AC2 of 0.46 ( 95% CI: 0.24 to 0.69). On the other side, Gemini 2.5 Pro Preview – the model with higher consistency with human consensus – yielded an AC2 of 0.69 (95% CI: 0.54 to 0.84). With 95% confidence, three of the four tested LLMs achieved ‘moderate’ or higher reliability based on benchmarking. LLMs could be helpful in the risk-of-bias assessment of systematic reviews using the ROBUST-RCT tool.