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Updated: May 10, 2025

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Published on: December 6, 2024
Perspective review: Will generative AI make common data models obsolete in future analyses of distributed data
Jeffery L Painter1, Darmendra Ramcharran2, Andrew Bate3,4
1GSK, 410 Blackwell Street, Durham, NC 27701, USA.
Generative AI (GenAI) and knowledge graphs (KGs) may replace traditional Common Data Models (CDMs) for healthcare data analysis. This approach enables direct querying of raw data, overcoming CDM limitations and enhancing real-time insights.
Area of Science:
- Health Informatics
- Artificial Intelligence in Healthcare
- Data Science
Background:
- Integrating real-world healthcare data is complex due to diverse formats and terminologies, requiring resource-intensive standardization.
- Common Data Models (CDMs) improve interoperability but can lead to information loss, semantic inconsistencies, and high implementation/update costs.
Purpose of the Study:
- To explore how generative artificial intelligence (GenAI), particularly large language models (LLMs), can overcome limitations of CDMs in quantitative healthcare data analysis.
- To propose a fourth generation of distributed data network analysis leveraging GenAI and knowledge graphs (KGs).
Main Methods:
- Reviewing the potential of GenAI (LLMs) to interpret natural language queries and generate code for direct interaction with raw healthcare data.
- Integrating knowledge graphs (KGs) to standardize semantic relationships across heterogeneous data, preserving data integrity.
- Proposing a framework for a fourth generation of distributed data network analysis.
Main Results:
- GenAI can potentially make CDMs obsolete by enabling direct analysis of raw data through natural language queries and automated code generation.
- KGs can standardize semantics and relationships, preserving data integrity and enabling effective GenAI.
- A GenAI-enabled approach with KGs offers potential for efficient, real-time analyses across diverse datasets.
Conclusions:
- GenAI, combined with KGs, presents a promising alternative to CDMs for quantitative healthcare data analysis, enhancing efficiency and data integrity.
- Further research is recommended to assess the transformative potential of GenAI in healthcare data analysis, ensuring privacy, security, and governance.
- This approach aims to overcome current limitations in data standardization and analysis, ultimately enhancing patient safety.
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