构建实时公开的临床试验预测平台，推动AI提前预判医疗结果
CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction
- 用自动化LLM搜索识别试验结果最早公开时间，确保预测时结果未泄露
- 发布2025年冬夏两期测试集，支持真实时间序列预测挑战
- 开放平台允许任意方法与数据源，适合医疗AI与预测研究者
科学家长期致力于在真实事件发生前准确预测其结果。人工智能能否更可靠地实现这一目标？我们通过临床试验结果预测这一高风险开放挑战来探讨此问题。本文提出CT Open——一个每年举办四次挑战的开源、实时运行平台。任何人均可提交预测，平台在试验结果公开后评估其准确性。然而，确定某试验结果何时首次在互联网上公开极为困难：官方注册库信息可能延迟数年，而首次提及可能出现在非主流文章中。为此，我们设计了一种全新的全自动去污染流水线，利用迭代式LLM驱动的网络搜索，精准定位试验结果的最早公开记录。通过专家人工标注验证了该流水线的准确性和可靠性。由于该流程确保所有评估试验在预测提交时均无公开结果，参与者可自由使用任意方法和数据源。本文发布训练集及两个时间戳测试基准——2025年冬季与夏季。我们相信CT Open可成为推动AI在现实世界事件预测领域发展的核心枢纽，同时为生物医学研究与临床试验设计提供支持。平台地址：https://ct-open.net/
原文摘要 · Abstract (English)
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 the earliest mention of trial outcomes. We validate the pipeline's quality and accuracy by human expert's annotations. Since CT Open's pipeline ensures that every evaluated trial had no publicly reported outcome when the prediction was made, it allows participants to use any methodology and any data source. In this paper, we release a training set and two time-stamped test benchmarks, Winter 2025 and Summer 2025. We believe CT Open can serve as a central hub for advancing AI research on forecasting real-world outcomes before they occur, while also informing biomedical research and improving clinical trial design. CT Open Platform is hosted at $\href{https://ct-open.net/}{https://ct-open.net/}$
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