MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

• URL: http://arxiv.org/abs/2304.08247v2
• Date: Wed, 4 Oct 2023 23:28:00 GMT
• Title: MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data
• Authors: Tianyu Han and Lisa C. Adams and Jens-Michalis Papaioannou and Paul Grundmann and Tom Oberhauser and Alexander L\"oser and Daniel Truhn and Keno K. Bressem
• Abstract summary: Large language models (LLMs) hold considerable promise for improving medical, 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. We present an innovative dataset consisting of over 160,000 entries, specifically crafted to fine-tune LLMs for effective medical applications.
• Score: 40.97474177100237
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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