# Empowering Clinical Trial Design through AI: A Randomized Evaluation of PowerGPT

arXiv: https://arxiv.org/abs/2509.12471 · HTML: https://mlsanity.com/paper/2509.12471/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 1 of 1)

- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)

## Built on by (most cited first, 1 of 1)

- [Polar: LLM-Powered Synthesis of Real-World Cyber Evidence for Prioritization and Mitigation](https://mlsanity.com/paper/2610.07298/) (2026)

## Abstract (arXiv)

Sample size calculations for power analysis are critical for clinical research and trial design, yet their complexity and reliance on statistical expertise create barriers for many researchers. We introduce PowerGPT, an AI-powered system integrating large language models (LLMs) with statistical engines to automate test selection and sample size estimation in trial design. In a randomized trial to evaluate its effectiveness, PowerGPT significantly improved task completion rates (99.3% vs. 88.9% for test selection, 99.3% vs. 77.8% for sample size calculation) and accuracy (94.1% vs. 55.4% in sample size estimation, p < 0.001), while reducing average completion time (4.0 vs. 9.3 minutes, p < 0.001). These gains were consistent across various statistical tests and benefited both statisticians and non-statisticians as well as bridging expertise gaps. Already under deployment across multiple institutions, PowerGPT represents a scalable AI-driven approach that enhances accessibility, efficiency, and accuracy in statistical power analysis for clinical research.