AI-Enhanced Adaptive Designs for Rare Diseases

Abstract

Rare diseases, affecting a small percentage of the population, present unique challenges in clinical trial design due to limited patient availability and disease heterogeneity. Traditional clinical trial designs often prove inadequate for rare disease research due to their rigid structure and large sample size requirements. This research explores the potential of AIenhanced adaptive designs to address these challenges and improve the efficiency and success of rare disease clinical trials. Adaptive designs offer flexibility by allowing modifications to trial parameters during the study's progression based on accumulating data. This adaptability is particularly valuable in rare disease settings where initial assumptions about disease characteristics or treatment effects may be uncertain. This study investigates how artificial intelligence can augment adaptive designs for rare diseases. AI algorithms can analyze diverse data sources, including patient demographics, medical history, genetic information, and real-world data, to identify relevant subpopulations and predict treatment responses. By integrating AI into adaptive designs, researchers can refine patient selection criteria, optimize treatment allocation, and adjust sample sizes dynamically, ultimately leading to more efficient and informative trials. The potential benefits of AI-enhanced adaptive designs include reduced development time and costs, improved patient outcomes, and increased likelihood of successful trials. This research reviews the current literature on adaptive designs and AI in clinical trials, proposes a framework for integrating AI into adaptive designs for rare diseases, and discusses the ethical and regulatory considerations associated with this approach. The goal is to demonstrate how AI can enhance the efficiency and effectiveness of rare disease clinical trials, ultimately accelerating the development of new therapies for patients with these conditions.