  Recent advancements in large language models (LLMs) such as ChatGPT and LLaMA
have hinted at their potential to revolutionize medical applications, yet their
application in clinical settings often reveals limitations due to a lack of
specialized training on medical-specific data. In response to this challenge,
this study introduces Me-LLaMA, a novel medical LLM family that includes
foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions -
Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction
tuning of LLaMA2 using large medical datasets. Our methodology leverages a
comprehensive domain-specific data suite, including a large-scale, continual
pre-training dataset with 129B tokens, an instruction tuning dataset with 214k
samples, and a new medical evaluation benchmark (MIBE) across six critical
medical tasks with 12 datasets. Our extensive evaluation using the MIBE shows
that Me-LLaMA models achieve overall better performance than existing
open-source medical LLMs in zero-shot, few-shot and supervised learning
abilities. With task-specific instruction tuning, Me-LLaMA models outperform
ChatGPT on 7 out of 8 datasets and GPT-4 on 5 out of 8 datasets. In addition,
we investigated the catastrophic forgetting problem, and our results show that
Me-LLaMA models outperform other open-source medical LLMs in mitigating this
issue. Me-LLaMA is one of the largest open-source medical foundation LLMs that
use both biomedical and clinical data. It exhibits superior performance across
both general and medical tasks compared to other open-source medical LLMs,
rendering it an attractive choice for medical AI applications. We release our
models, datasets, and evaluation scripts at:
https://github.com/BIDS-Xu-Lab/Me-LLaMA.
