The New England Journal of Statistics in Data Science

Artificial intelligence (AI) holds great promise for supporting clinical trials, from patient recruitment and endpoint assessment to treatment response prediction. However, deploying AI without safeguards poses significant risks, particularly when evaluating patient endpoints that directly impact trial conclusions. We compared two AI frameworks against human-only assessment for medical image-based disease evaluation, measuring cost, accuracy, robustness, and generalization ability. To stress-test these frameworks, we injected bad models—ranging from random guesses to naive predictions—to ensure that observed treatment effects remain valid even under severe model degradation. We evaluated the frameworks using two randomized controlled trials with endpoints derived from spinal X-ray images. Our findings indicate that using AI as a supporting reader (AI-SR) is the most suitable approach for clinical trials, as it meets all criteria across various model types, even with bad models. This method consistently provides reliable disease estimation, preserves clinical trial treatment effect estimates and conclusions, and retains these advantages when applied to different populations.

The MiniPDX model is a cost-effective preclinical platform that replicates human tumor microenvironments in mice, enabling rapid drug response evaluation in precision oncology. In contrast, the patient-derived xenograft (PDX) model offers a more comprehensive but slower and costlier approach by engrafting human tumors into immunodeficient mice to evaluate efficacy in a physiologically relevant context. In this study, we present an integrated Bayesian framework that supports both separate and joint analyses of MiniPDX and PDX data. Our models borrow information across biomarker-defined subgroups and between assays, improving the efficiency and precision of drug effect estimation. Furthermore, we implement an adaptive sample size re-estimation procedure for MiniPDX experiments, allowing for more flexible designs and optimized resource utilization. Together, these methods provide a Bayesian framework that improves estimation efficiency for small-sample MiniPDX and PDX experiments and offers a principled adaptive sample-size rule, helping to identify biomarker-defined subpopulations with distinct predictive profiles for treatment response.