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arXiv · 2511.03048

ROBoto2: An Interactive System and Dataset for LLM-assisted Clinical Trial Risk of Bias Assessment

Anthony Hevia·Sanjana Chintalapati·Veronica Ka Wai Lai·Thanh Tam Nguyen·Wai-Tat Wong·Terry Klassen·Lucy Lu Wang
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Abstract

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.

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Anthony Hevia, Sanjana Chintalapati, Veronica Ka Wai Lai, Thanh Tam Nguyen, Wai-Tat Wong, Terry Klassen, Lucy Lu Wang. 2025-11-04. ROBoto2: An Interactive System and Dataset for LLM-assisted Clinical Trial Risk of Bias Assessment. https://arxiv.org/abs/2511.03048

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