Real-Time Hybrid Language Model For Virtual Patient Conversations

Advancements in deep learning have enabled the development of online learning tools for medical training, which is important for remote learning. However, face-to-face interaction is essential for practicing human-centric skills such as clinical skills. Presently, in medical training, such interactions can be mimicked using deep learning methodologies. However, the understanding of such models is often limited whereby lightweight models are unable to generalize beyond scope while large language models tend to produce unexpected responses. To overcome this, we propose a hybrid lightweight and large language model for creating virtual patients, which can be used for real-time autonomous training of trainee doctors in clinical settings using online platforms. This ensures high-quality and standardized learning for all individuals regardless of location and background. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.