Data reuse enables cost-efficient randomized trials of medical AI models

• URL: http://arxiv.org/abs/2511.08986v2
• Date: Thu, 13 Nov 2025 20:12:08 GMT
• Title: Data reuse enables cost-efficient randomized trials of medical AI models
• Authors: Michael Nercessian, Wenxin Zhang, Alexander Schubert, Daphne Yang, Maggie Chung, Ahmed Alaa, Adam Yala,
• Abstract summary: We propose BRIDGE, a data-reuse RCT design for AI-based risk models.<n>BRIDGE trials recycle participant-level data from completed trials of AI models when legacy and updated models make concordant predictions.<n>We simulate a series of breast cancer screening studies, where our design reduced required enrollment by 46.6%--saving over US$2.8 million--while maintaining 80% power.
• Score: 38.36499561588967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Randomized controlled trials (RCTs) are indispensable for establishing the clinical value of medical artificial-intelligence (AI) tools, yet their high cost and long timelines hinder timely validation as new models emerge rapidly. Here, we propose BRIDGE, a data-reuse RCT design for AI-based risk models. AI risk models support a broad range of interventions, including screening, treatment selection, and clinical alerts. BRIDGE trials recycle participant-level data from completed trials of AI models when legacy and updated models make concordant predictions, thereby reducing the enrollment requirement for subsequent trials. We provide a practical checklist for investigators to assess whether reusing data from previous trials allows for valid causal inference and preserves type I error. Using real-world datasets across breast cancer, cardiovascular disease, and sepsis, we demonstrate concordance between successive AI models, with up to 64.8% overlap in top 5% high-risk cohorts. We then simulate a series of breast cancer screening studies, where our design reduced required enrollment by 46.6%--saving over US$2.8 million--while maintaining 80% power. By transforming trials into adaptive, modular studies, our proposed design makes Level I evidence generation feasible for every model iteration, thereby accelerating cost-effective translation of AI into routine care.

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