CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction
Jianyou Wang, Youze Zheng, Longtian Bao, Hanyuan Zhang, Qirui Zheng, Yuhan Chen, Yang Zhang, Matthew Feng, Maxim Khan, Aditya K. Sehgal, Christopher D. Rosin, Ramamohan Paturi, Umber Dube, Leon Bergen

TL;DR
CT Open is a pioneering open platform for real-time clinical trial outcome prediction, utilizing automated web search to ensure unbiased evaluation of AI models on unseen trial results.
Contribution
It introduces a novel automated decontamination pipeline and provides publicly available benchmarks for future AI research in clinical outcome forecasting.
Findings
Automated pipeline accurately identifies earliest trial outcome mentions.
Platform enables unbiased evaluation of predictive models on unseen data.
Provides training data and benchmarks for future research.
Abstract
Scientists have long sought to accurately predict outcomes of real-world events before they happen. Can AI systems do so more reliably? We study this question through clinical trial outcome prediction, a high-stakes open challenge even for domain experts. We introduce CT Open, an open-access, live platform that will run four challenge every year. Anyone can submit predictions for each challenge. CT Open evaluates those submissions on trials whose outcomes were not yet public at the time of submission but were made public afterwards. Determining if a trial's outcome is public on the internet before a certain date is surprisingly difficult. Outcomes posted on official registries may lag behind by years, while the first mention may appear in obscure articles. To address this, we propose a novel, fully automated decontamination pipeline that uses iterative LLM-powered web search to identify…
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