MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data
This addresses the problem of patient privacy and accessibility in medical AI by providing deployable open-source models for healthcare professionals.
The authors tackled the need for open-source medical conversational AI by creating a dataset of over 160,000 entries to fine-tune LLMs, and found that fine-tuned models outperformed pre-trained-only models on medical certification exams.
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.