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ChatGPT and Gemini large language models for pharmacometrics with NONMEM: comment
Hinpetch Daungsupawong1, Viroj Wiwanitkit2
1Lao People's Democratic Republic, Phonhong, Laos. hinpetchdaung@gmail.com.
This correspondence evaluates ChatGPT and Gemini large language models for pharmacometrics using NONMEM. It also provides additional concerns regarding their application in this field.
Area of Science:
- Pharmacometrics and Computational Science
- Artificial Intelligence in Drug Development
Background:
- The study addresses the emerging use of large language models (LLMs) like ChatGPT and Gemini in pharmacometric analyses.
- It specifically focuses on their application within the NONMEM (Non-linear Mixed Effects Modeling) software environment, a standard in drug development.
Discussion:
- Concerns are raised regarding the reliability and accuracy of LLM-generated outputs for pharmacometric modeling.
- The correspondence highlights potential pitfalls and limitations when integrating these advanced AI tools into established workflows.
Key Insights:
- Evaluation of ChatGPT and Gemini for pharmacometric tasks using NONMEM.
- Identification of specific concerns and challenges associated with their implementation.
- Correspondence provides critical user feedback on LLM utility in quantitative pharmacology.
Outlook:
- Further research is needed to validate LLM performance in pharmacometrics.
- Development of best practices for utilizing AI tools like ChatGPT and Gemini in drug development is essential.
- Future work should focus on ensuring the safety and efficacy of AI-assisted pharmacometric analyses.
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