ROBoto2: An Interactive System and Dataset for LLM-assisted Clinical Trial Risk of Bias Assessment
This addresses the problem of automating systematic reviews for researchers and clinicians, but it is incremental as it builds on existing methods with a new dataset and interface.
The authors tackled the labor-intensive process of risk of bias assessment in clinical trials by developing ROBOTO2, an interactive system that uses LLMs to assist with annotations, resulting in a dataset of 521 trials and benchmarking showing current model capabilities and challenges.
We present ROBOTO2, an open-source, web-based platform for large language model (LLM)-assisted risk of bias (ROB) assessment of clinical trials. ROBOTO2 streamlines the traditionally labor-intensive ROB v2 (ROB2) annotation process via an interactive interface that combines PDF parsing, retrieval-augmented LLM prompting, and human-in-the-loop review. Users can upload clinical trial reports, receive preliminary answers and supporting evidence for ROB2 signaling questions, and provide real-time feedback or corrections to system suggestions. ROBOTO2 is publicly available at https://roboto2.vercel.app/, with code and data released to foster reproducibility and adoption. We construct and release a dataset of 521 pediatric clinical trial reports (8954 signaling questions with 1202 evidence passages), annotated using both manually and LLM-assisted methods, serving as a benchmark and enabling future research. Using this dataset, we benchmark ROB2 performance for 4 LLMs and provide an analysis into current model capabilities and ongoing challenges in automating this critical aspect of systematic review.