Introduction:
Over the years, the use of artificial intelligence (AI) in medical research has shown great promise in enhancing drug discovery, identifying new treatment targets, and predicting disease outcomes1. AI is an umbrella term encompassing several advanced technologies, such as machine learning, natural language processing, and deep learning. These methods facilitate the extraction of patterns and insights from vast amounts of data. A recent exciting development in AI research has been the public release of ChatGPT2, developed by OpenAI. The model architecture behind ChatGPT (GPT; Generative Pre-trained Transformer3 has shown to be very capable of achieving strong natural language understanding, while its accessible graphical user interface has resulted in widespread adoption.
Large Language Models (LLMs) such as ChatGPT are trained on an enormous corpus of text in order to generative responses to queries4. By devoting considerable human time labeling the quality of generated responses and re-training the model to produce the best responses, ChatGPT has suprised many to produce fluent and accurate responses to human inquiries. Aside from the public interest in the use of ChatGPT, there has also been suggestions of using the model to assist students and researchers by editing text, answering questions, writing code, and finding relevant literature given a query5–8.
There already exist several publications discussing the potential impact of LLMs on a wide range of different research fields9–11. It however remains unknown if tools like ChatGPT can also support researchers from relatively small research fields, potentially resulting from a lower availability of training data. In this work, we investigate if ChatGPT can be used to assist during the development of population pharmacokinetic (PK) models. As an use-case, we use ChatGPT to generate R code for predicting in vivo drug concentrations of standard half-life factor VIII (FVIII) concentrates in patients with haemophilia A12. Next, we query ChatGPT to generate an interactive R shiny application that can be used for the interpretation of the model and the selection of optimal doses. Based on this use-case, we aim to show that researchers unfamiliar with programming in R can nonetheless produce usable code for data analysis.