Abstract

This report examines cutting-edge technological innovations in clinical trial intelligence, with a focused analysis of advancements emerging from Shenzhen and the Greater Bay Area. By leveraging the region’s robust AI and biotechnology ecosystems, this study addresses critical bottlenecks in clinical trial processes while aligning with the “Double Ten Law” in pharmaceutical research and development.

Central to this research is the AI Module (AIM), a Large Language Model (LLM)-based intelligent clinical trial design system. AIM automates trial protocol generation, parses structured medical documents and literature, selects inclusion/exclusion criteria and endpoints, and evaluates trial feasibility, patient recruitment, retention rates, and multilingual document generation. Additionally, it integrates data and process management tools alongside predictive analytics algorithms for trial outcome forecasting, employing AI for Science methodologies to optimize decision-making.

The report further explores regulatory and ethical considerations in AI-driven clinical trials, proposing an expert-guided adaptive design framework that incorporates dark data and experiential learning. This framework enhances scalability for multicenter, cross-regional trials by enabling data-driven dynamic parameter updates, providing transparent explanations for trial outcomes, and generating robust evidence to support drug development. AIM’s architecture embeds regulatory requirements, technical guidelines, and registration frameworks from ICH member jurisdictions (including China, the EU, the US, Japan, and Canada), offering intelligent recommendations for region-specific regulatory pathways.

These findings highlight the transformative potential of AI for Science in accelerating translational medicine. The study not only advances scholarly understanding of AI applications in clinical research but also delivers actionable insights for stakeholders across the pharmaceutical and healthcare industries.