Designing a Custom GPT Model for Medical Dialogue Simulation

Abstract

The introduction of the Licence-Master-Doctorate (LMD) system in Africa, particularly in Burkina Faso, has created challenges for medical training due to a lack of infrastructure and personnel. This study develops a custom GPT model for medical dialogue simulation, overcoming the limitations of a previous MLP model. A structured dataset was designed, grouping diseases into modules and defining patient profiles. An open-source GPT-2 model was modified and trained, with user interaction facilitated through an API. After initial training, the model showed promising results, indicating stable learning. This model offers better context management and customization suited for medical dialogues. Future improvements include expanding to other pathologies and optimizing performance for effective integration into medical training in Africa.