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AI for NONMEM Coding in Pharmacometrics Research and Education: Shortcut or Pitfall?
Wenhao Zheng1, Wanbing Wang2,3, Carl M J Kirkpatrick4
1Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Large Language Models (LLMs) show promise in generating NONMEM codes for pharmacometric modeling, with OpenAI models excelling when using optimized prompts. Human oversight remains crucial for complex models and preventing AI overreliance in pharmacometrics education.
Area of Science:
- Pharmacometrics and Computational Science
- Artificial Intelligence in Drug Development
- Clinical Pharmacology Modeling
Background:
- Pharmacometric modeling relies on complex coding, often using NONMEM, presenting a significant challenge.
- Artificial intelligence (AI), specifically Large Language Models (LLMs), offers potential solutions to automate and streamline NONMEM code generation.
- Evaluating LLM capabilities is crucial for integrating AI into pharmacometric workflows.
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