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

TL;DR

The paper introduces BRIDGE, a data-reuse RCT design that reduces costs and time for validating medical AI models by recycling participant data from previous trials when models make similar predictions.

Contribution

BRIDGE enables cost-efficient, adaptive RCTs for AI models by leveraging existing data, facilitating faster validation of model updates and iterations.

Findings

Up to 64.8% overlap in high-risk cohorts across models

Reduced trial enrollment by 46.6% in breast cancer screening

Saved over US$2.8 million in simulated studies

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.