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Application of Artificial Intelligence in MedDRA Coding: A Practical Exploration from Clinical Data Management

Charles Yan1, Ruier Yang2, Huaihai Yan2

  • 1Clinical Data Science Center, Hengrui Pharmaceutical Co., Ltd., Shanghai, 201203, China. charles.yan@hengrui.com.

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|March 22, 2026
PubMed
Summary

Artificial intelligence (AI) significantly enhances Medical Dictionary for Regulatory Activities (MedDRA) coding in clinical data management (CDM), reducing coding time and manual workload. This AI-driven approach improves efficiency, consistency, and compliance in drug development.

Keywords:
Artificial intelligenceClinical data managementLarge language modelsMedDRA codingNatural language processingRetrieval-augmented generation

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Area of Science:

  • Clinical Data Management
  • Artificial Intelligence in Pharmaceuticals
  • Regulatory Compliance

Background:

  • Manual MedDRA coding in clinical data management (CDM) presents challenges in data quality, efficiency, and consistency.
  • Complexity of terminology and frequent dictionary updates further impede timely trial progress and compliance.

Purpose of the Study:

  • To identify and validate artificial intelligence (AI) technologies for MedDRA coding within CDM.
  • To address technical and regulatory challenges associated with AI implementation in MedDRA coding.

Main Methods:

  • Developed an integrated Retrieval-Augmented Generation (RAG)-AI-Agent framework using Large Language Models (LLMs) for automated MedDRA coding.
  • Utilized real-world adverse event (AE) data for spelling error detection and automated coding, evaluating performance metrics like Precision and coding time.

Main Results:

  • AI achieved 100% Precision for spelling detection and reduced manual review workload by approximately 70%.
  • Automated coding decreased average coding time by 48.7% with 95.8% AE term coverage and 70% precision against manual standards.
  • Ensured compliance through local deployment and 21 CFR Part 11-aligned audit trails.

Conclusions:

  • The AI-enhanced framework substantially boosts CDM efficiency, consistency, and regulatory compliance.
  • This study offers a replicable model for pharmaceutical digital transformation, accelerating drug development and enhancing patient safety.
  • Future work should expand validation across diverse therapeutic areas and languages.