Insights into the adoption of innovative clinical trials across therapeutic areas using clinical trials registry data and large Language models.

Innovative clinical trial designs, such as adaptive and Bayesian methodologies, have gained traction as solutions to the challenges of traditional trials, including their high costs and complex regulations. When they adhere to relevant ethical and regulatory requirements, these designs can improve efficiency, flexibility, and ethical standards. However, their application outside of oncology, particularly in fields such as neuroscience and rare diseases, remains underexplored. We analyzed data from ClinicalTrials.gov for interventional trials registered between 2005 and 2024. The trials were classified as innovative or traditional using a keyword-based algorithm. Therapeutic areas were identified using a large language model (LLM), with classification accuracy evaluated using a random sample of 2,000 trials. Of the 348,818 trials, 5827 were classified as innovative, with prevalence in neuroscience and rare diseases. These designs were predominantly observed in early-phase trials and pediatric research, with limited representation in elderly-focused or sex-specific studies. Innovative trial adoption has grown since 2011, spurred by regulatory advancements and increased funding from scientific networks and the National Institutes of Health. Survival analysis revealed that innovative trials tend to remain active for longer than traditional trials; however, this trend varies across different medical disciplines. LLM demonstrated a classification accuracy of 94.6% (95%CI = 93.6%-95.5%), supporting its utility for trial categorization. The rise in innovative clinical trial designs reflects a shift toward addressing complex challenges in neuroscience, rare diseases, and other therapeutic areas. Although these designs show promise in improving trial efficiency and patient outcomes, their success depends on rigorous planning and adherence to regulatory standards. Advancing LLM-based tools can further optimize clinical trial monitoring by tailoring research in trial settings and therapeutic fields.