As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fields. In medicine, these LLMs hold considerable promise for improving medical workflows, diagnostics, patient care, and education. Yet, there is an urgent need for open-source models that can be deployed on-premises to safeguard patient privacy. In our work, we present an innovative dataset consisting of over 160,000 entries, specifically crafted to fine-tune LLMs for effective medical applications. We investigate the impact of fine-tuning these datasets on publicly accessible pre-trained LLMs, and subsequently, we juxtapose the performance of pre-trained-only models against the fine-tuned models concerning the examinations that future medical doctors must pass to achieve certification.
@article{arxiv.2304.08247,
title = {MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data},
author = {Tianyu Han and Lisa C. Adams and Jens-Michalis Papaioannou and Paul Grundmann and Tom Oberhauser and Alexei Figueroa and Alexander Löser and Daniel Truhn and Keno K. Bressem},
journal= {arXiv preprint arXiv:2304.08247},
year = {2025}
}