BACKGROUND: At the end of 2023, Bayer AG launched its own internal large language model (LLM), MyGenAssist, based on ChatGPT technology to overcome data privacy concerns. It may offer the possibility to decrease their harshness and save time spent on repetitive and recurrent tasks that could then be dedicated to activities with higher added value. Although there is a current worldwide reflection on whether artificial intelligence should be integrated into pharmacovigilance, medical literature does not provide enough data concerning LLMs and their daily applications in such a setting. Here, we studied how this tool could improve the case documentation process, which is a duty for authorization holders as per European and French good vigilance practices. OBJECTIVE: The aim of the study is to test whether the use of an LLM could improve the pharmacovigilance documentation process. METHODS: MyGenAssist was trained to draft templates for case documentation letters meant to be sent to the reporters. Information provided within the template changes depending on the case: such data come from a table sent to the LLM. We then measured the time spent on each case for a period of 4 months (2 months before using the tool and 2 months after its implementation). A multiple linear regression model was created with the time spent on each case as the explained variable, and all parameters that could influence this time were included as explanatory variables (use of MyGenAssist, type of recipient, number of questions, and user). To test if the use of this tool impacts the process, we compared the recipients' response rates with and without the use of MyGenAssist. RESULTS: An average of 23.3% (95% CI 13.8%-32.8%) of time saving was made thanks to MyGenAssist (P<
.001
adjusted R CONCLUSIONS: Our study is the first to show that a ChatGPT-based tool can improve the efficiency of a good practice activity without needing a long training session for the affected workforce. These first encouraging results could be an incentive for the implementation of LLMs in other processes.