{"ID":2842613,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08986","arxiv_id":"2511.08986","title":"Data reuse enables cost-efficient randomized trials of medical AI models","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.08986","url_pdf":"https://arxiv.org/pdf/2511.08986v2","authors":"[\"Michael Nercessian\",\"Wenxin Zhang\",\"Alexander Schubert\",\"Daphne Yang\",\"Maggie Chung\",\"Ahmed Alaa\",\"Adam Yala\"]","published":"2025-11-12T05:09:00Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.AP\",\"stat.ME\"]","methods":"[\"Large Language Model\"]","has_code":false}