  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.
