PS51: Statistical Guardrails for Large Language Models in Clinical Trial Design and Patient Recruitment

Clinical trial recruitment and design are increasingly exploring large language models (LLMs) to automate complex tasks such as patient-to-trial matching, eligibility assessment, and study design support. Frameworks like TrialGPT and Panacea demonstrate the potential to accelerate operations, reduce screening time, and provide interpretable outputs. Yet, their adoption must be guided by rigorous statistical methodology to ensure clinical and regulatory credibility. From a statistical perspective, patient matching is a classification problem where miscalibration, bias propagation, and unstable variance can undermine validity. Probabilistic scoring, sensitivity analyses, and uncertainty quantification offer a principled foundation, while causal inference and Bayesian frameworks can help align model-generated criteria with historical evidence. For trial design tasks, reproducibility and generalizability must be evaluated against robust benchmarks. Incorporating cross-validation, out-of-sample checks, and covariate-adjusted modeling can mitigate overfitting and hidden bias. Guardrails also extend to data integrity. Machine learning–based anomaly detection within electronic data capture systems has achieved >85% sensitivity in detecting fabricated or erroneous values, illustrating how statistical oversight strengthens trust in AI-driven workflows. This session highlights how bias control, uncertainty quantification, and ethical safeguards are not optional but essential for LLMs to evolve from exploratory tools into reliable, regulatory-grade infrastructure for clinical trials.