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Related Experiment Video

Updated: Jun 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Leveraging generative AI to transform statistical analysis plan authoring in clinical trials.

Rogier Landman¹, Birol Emir¹, Richard Zhang¹

¹Pfizer Inc, New York, NY, USA.

Clinical Trials (London, England)

|March 4, 2026

Summary

This summary is machine-generated.

Generative AI significantly reduces statistical analysis plan (SAP) drafting time from weeks to minutes, improving consistency and quality in clinical research documentation.

Keywords:

Generative AI authoring automation clinical trial documentation large language models protocol regulatory documents statistical analysis plan

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Last Updated: Jun 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Clinical Research Operations
Regulatory Science
Artificial Intelligence in Healthcare

Background:

Statistical analysis plans (SAPs) are crucial regulatory documents for clinical trials, traditionally requiring extensive time (4-6 weeks) for development.
Complex trial designs (adaptive, real-world evidence) and evolving regulations increase SAP development challenges.
Generative AI offers potential for streamlining SAP authoring, but requires stringent accuracy and consistency controls in regulated environments.

Purpose of the Study:

To design and implement a generative AI-based solution for automated SAP drafting.
To evaluate the efficiency, quality, and consistency of AI-generated SAPs across various clinical trial designs.

Main Methods:

Developed a generative AI system using a knowledge graph and vector database to process clinical protocols.
Employed a hybrid generation strategy: verbatim copying, summarization, dynamic variable insertion, and de novo text generation.
Deployed as a web application and Microsoft Word add-in; evaluated on 71 SAPs across diverse trial types.

Main Results:

Reduced SAP first draft generation time to an average of 1.0-3.4 minutes, a significant improvement over the traditional 1-2 days.
Subject matter experts rated AI-generated SAP quality between 3.6-4.2 (5-point scale), indicating moderate to high quality.
Achieved an average semantic similarity score of 0.75 compared to manual SAPs, demonstrating substantial meaning preservation and high document consistency.

Conclusions:

Generative AI application in SAP authoring substantially advances clinical research operations by improving drafting speed and documentation consistency.
Automated text generation offers potential benefits for downstream processes like statistical programming and clinical study report drafting.
Future work should focus on enhancing automated quality assurance, auditable amendment handling, and deeper integration into clinical documentation workflows.