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

By T. Han, L. Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, D. Truhn, K. Bressem. 2023

462 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/90e41626b8c78600da70c4350c67c3a10525cb37

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jianfeng Gao
2. Chunyuan Li
3. Jianwei Yang
4. Haotian Liu
5. Wei Xiong
6. Hoifung Poon
7. Zhe Gan
8. T. Zhang

Citations in the corpus

• A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, unknown): “To tackle this challenge and avert unauthorized data transfers, MedAlpaca [186] employed an opensource policy that enables on-site implementation, aiming at mitigating privacy concerns.”
• Adapted large language models can outperform medical experts in clinical text summarization (2023, unknown): “Given the model’s lower error rate in each category, this suggests that incorporating LLMs could actually reduce fabricated information in clinical practice.”
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day (2023, background): “…by ChatGPT [34]/GPT-4 [35] and the success of open-sourced instruction-tuned large language models (LLMs) in the general domain, several biomedical LLM chatbots have been developed, including ChatDoctor [49], Med-Alpaca [13], PMC-LLaMA [47], Clinical Camel [1], DoctorGLM [48], and Huatuo [46].”
• LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models (2023, background): “LMFlow provides a complete finetuning workflow for these four stages, supporting large language models’ personalized training with limited computing resources.”