Traditional clinical trial monitoring, reliant on labor-intensive site visits and manual data review via Electronic Data Capture systems, is both time-consuming and resource-intensive. The emergence of risk-based monitoring (RBM) and quality tolerance limit (QTL) frameworks offers a more efficient alternative by proactively identifying systematic risks to patient safety and data integrity. In this paper, we propose an advanced machine learning (ML) approach to enable real-time, automated QTL risk assessment. The QTL-ML framework integrates multi-domain clinical data to predict diverse QTLs at the program, study, site, and patient levels, bypassing limitations of static thresholds and single-source data dependency. Our assumption-free approach leverages dynamically accumulating trial data to detect and mitigate risks in an automated manner. Embedded within ICH-E6(R2) RBM principles, this innovative solution enhances patient safety, reduces trial durations, and curbs costs. Furthermore, we introduce an extension leveraging a deep learning framework, incorporating hierarchical anomaly detection and temporal analysis to enhance accuracy and scalability across clinical trial settings. Together, these methodologies hold transformative potential for the efficacy and sustainability of clinical trial monitoring.

Writing statistical analysis plans (SAPs) is often time-consuming, particularly for junior statisticians new to the pharmaceutical industry. As a standard internal practice, a well-developed SAP draft must be finalized before first subject in. Delays in this process can hinder the timely integration of statistical strategy into study design and execution. Furthermore, despite having a standard SAP template, inconsistencies in practice still occur—potentially compromising document quality and creating misinterpretations during collaboration with programming teams. With increasing demands to accelerate development while reducing cost and timelines, a smarter, faster, and more consistent approach is urgently needed. We present a retrieval-augmented generation (RAG) powered large language model (LLM) system that automatically generates a protocol-specific SAP. To address the challenge of LLM hallucinations, we explored multiple optimization strategies within the RAG architecture to ensure stable and reliable output. Our approach leverages semantic search, vector embeddings, and domain-tuned retrieval to extract protocol-specific details, which are then integrated into a pre-aligned SAP template through the LLM. The resulting system delivers measurable improvements in efficiency, cost savings, and compliance—while maintaining scientific rigor through human-in-the-loop interactive review.