ArticleJournal of pharmacokinetics and pharmacodynamics2024
Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model.
Article in Journal of pharmacokinetics and pharmacodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Generative Artificial Intelligence for Data Analysis: A Randomised Controlled Trial in a Public Health Research Institute.International journal of public health · 2025Trial
- Context Engineering for AI-Assisted Pharmacometrics: A Practical Tutorial.CPT: pharmacometrics & systems pharmacology · 2026Article
- ChatGPT-4's Consistency, Specificity, and Inclusion of Behavior Change Techniques in Delivering Smoking Cessation Advice in Traditional Chinese: A Content Analysis.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026Article
- Retrieval Augmented Generation (RAG) for Evaluating Regulatory Compliance of Drug Information and Clinical Trial Protocols.CPT: pharmacometrics & systems pharmacology · 2026Article
- Large Language Models for Clinical Trial Protocol Assessments.Clinical pharmacology and therapeutics · 2026Article
- Retrieval Augmented Generation (RAG) for Natural Language Querying of Immunogenicity Data for Protein Drugs.The AAPS journal · 2026Article
- Pharmacometrics in the Age of Large Language Models: A Vision of the Future.Pharmaceutics · 2025Article
- Evaluation of Six Large Language Models for Clinical Decision Support: Application in Transfusion Decision-making for RhD Blood-type Patients.Annals of laboratory medicine · 2025Article
- The dawn of a new era: can machine learning and large language models reshape QSP modeling?Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Leveraging large language models in pharmacometrics: evaluation of NONMEM output interpretation and simulation capabilities.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Leveraging In Silico and Artificial Intelligence Models to Advance Drug Disposition and Response Predictions Across the Lifespan.Clinical and translational science · 2025Review
- Characterizing patients at higher cardiovascular risk for prescribed stimulants: Learning from health records data with predictive analytics and data mining techniques.Computers in biology and medicine · 2025Article
- Open Science at the generative AI turn: An exploratory analysis of challenges and opportunities.Quantitative science studies · 2025Article
- Comparative evaluation of artificial intelligence systems' accuracy in providing medical drug dosages: A methodological study.World journal of methodology · 2024Article
- Knowledge Graph-Based In-Context Learning for Advanced Fault Diagnosis in Sensor Networks.Sensors (Basel, Switzerland) · 2024Article
- Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications.Medicina (Kaunas, Lithuania) · 2024Review
- Chain of Thought Utilization in Large Language Models and Application in Nephrology.Medicina (Kaunas, Lithuania) · 2024Review
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To systematically assess the ChatGPT large language model on diverse tasks relevant to pharmacokinetic data analysis. ChatGPT was evaluated with prototypical tasks related to report writing, code generation, non-compartmental analysis, and pharmacokinetic word problems. The writing task consisted of writing an introduction for this paper from a draft title. The coding tasks consisted of generating R code for semi-logarithmic graphing of concentration-time profiles and calculating area under the curve and area under the moment curve from time zero to infinity. Pharmacokinetics word problems on single intravenous, extravascular bolus, and multiple dosing were taken from a pharmacokinetics textbook. Chain-of-thought and problem separation were assessed as prompt engineering strategies when errors occurred. ChatGPT showed satisfactory performance on the report writing, code generation tasks and provided accurate information on the principles and methods underlying pharmacokinetic data analysis. However, ChatGPT had high error rates in numerical calculations involving exponential functions. The outputs generated by ChatGPT were not reproducible: the precise content of the output was variable albeit not necessarily erroneous for different instances of the same prompt. Incorporation of prompt engineering strategies reduced but did not eliminate errors in numerical calculations. ChatGPT has the potential to become a powerful productivity tool for writing, knowledge encapsulation, and coding tasks in pharmacokinetic data analysis. The poor accuracy of ChatGPT in numerical calculations require resolution before it can be reliably used for PK and pharmacometrics data analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.