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Updated: May 24, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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LLM4ODM: Synthetic Clinical Study Data Generation for CDISC ODM.

Elyas Hussein1, Beshr Kaadan1, Martin Dugas1

  • 1Institute of Medical Informatics, Heidelberg University, Germany.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

LLM4ODM generates realistic, context-aware clinical study data using large language models and CDISC ODM metadata. This approach significantly reduces manual effort and improves data plausibility for study databases.

Keywords:
CDISC ODMGenerative AILLMStudy DatabasesSynthetic Data

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Area of Science:

  • Clinical data management
  • Artificial intelligence in healthcare

Background:

  • Generating realistic clinical study data for validation is challenging.
  • Manual data creation is time-consuming, and rule-based methods lack clinical plausibility.

Purpose of the Study:

  • To introduce LLM4ODM, a novel system for generating synthetic clinical study data.
  • To leverage large language models (LLMs) for context-aware data generation from CDISC ODM metadata.

Main Methods:

  • LLM4ODM transforms CDISC ODM metadata into structured prompts for LLMs (Gemini 1.5 Flash).
  • Generated data is returned as JSON subject records and validated for schema compliance, data types, logic, and temporal coherence.
  • The system is containerized using Docker for accessibility.

Main Results:

  • LLM4ODM demonstrated 100% schema adherence across ten diverse ODM datasets.
  • Achieved over 78% reduction in manual effort compared to traditional methods.
  • Expert review indicated superior clinical plausibility compared to rule-based generation.

Conclusions:

  • LLM4ODM offers an efficient and effective solution for synthetic clinical study data generation.
  • The system enhances data realism and plausibility, crucial for reliable study databases.
  • Open availability of code and resources promotes further research and adoption.