ArticleBMC medical research methodology2025
Using a large language model (ChatGPT) to assess risk of bias in randomized controlled trials of medical interventions: protocol for a pilot study of interrater agreement with human reviewers.
Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Information Specialist Roles in the Era of Large Language Models: Prompting Continued Professional Development.Campbell systematic reviews · 2026Article
- Human-AI collaboration enhances the performance of large language models in risk of bias assessment.BMC medical research methodology · 2026Article
- Using a Large Language Model (ChatGPT-4o) to Assess the Risk of Bias in Randomized Controlled Trials of Medical Interventions: Interrater Agreement With Human Reviewers.Cochrane evidence synthesis and methods · 2025Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRisk of bias (RoB) assessment is an essential part of systematic reviews that requires reading and understanding each eligible trial and RoB tools. RoB assessment is subject to human error and is time-consuming. Machine learning-based tools have been developed to automate RoB assessment using simple models trained on limited corpuses. ChatGPT is a conversational agent based on a large language model (LLM) that was trained on an internet-scale corpus and has demonstrated human-like abilities in multiple areas including healthcare. LLMs might be able to support systematic reviewing tasks such as assessing RoB. We aim to assess interrater agreement in overall (rather than domain-level) RoB assessment between human reviewers and ChatGPT, in randomized controlled trials of interventions within medical interventions.
methodsWe will randomly select 100 individually- or cluster-randomized, parallel, two-arm trials of medical interventions from recent Cochrane systematic reviews that have been assessed using the RoB1 or RoB2 family of tools. We will exclude reviews and trials that were performed under emergency conditions (e.g., COVID-19), as well as public health and welfare interventions. We will use 25 of the trials and human RoB assessments to engineer a ChatGPT prompt for assessing overall RoB, based on trial methods text. We will obtain ChatGPT assessments of RoB for the remaining 75 trials and human assessments. We will then estimate interrater agreement using Cohen's κ.
resultsThe primary outcome for this study is overall human-ChatGPT interrater agreement. We will report observed agreement with an exact 95% confidence interval, expected agreement under random assessment, Cohen's κ, and a p-value testing the null hypothesis of no difference in agreement. Several other analyses are also planned.
conclusionsThis study is likely to provide the first evidence on interrater agreement between human RoB assessments and those provided by LLMs and will inform subsequent research in this area.
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