Me-LLaMA: Medical Foundation Large Language Models for Comprehensive Text Analysis and Beyond

Recent advancements in large language models (LLMs) like ChatGPT and LLaMA have shown significant potential in medical applications, but their effectiveness is limited by a lack of specialized medical knowledge due to general-domain training. In this study, we developed Me-LLaMA, a new family of open-source medical LLMs that uniquely integrate extensive domain-specific knowledge with robust instruction-following capabilities. Me-LLaMA comprises foundation models (Me-LLaMA 13B and 70B) and their chat-enhanced versions, developed through comprehensive continual pretraining and instruction tuning of LLaMA2 models using both biomedical literature and clinical notes. Me-LLaMA utilized the largest and most comprehensive medical data, including 129B pre-training tokens and 214K instruction tuning samples from diverse biomedical and clinical data sources. Training the 70B models required substantial computational resources, exceeding 100,000 A100 GPU hours. We applied Me-LLaMA to six medical text analysis tasks and evaluated its performance on 12 benchmark datasets. To further assess Me-LLaMA's potential clinical utility, we evaluated its performance on complex clinical case diagnosis compared with other commercial LLMs, using both automatic and human evaluations. Me-LLaMA models outperform LLaMA, and other existing open-source medical LLMs in both zero-shot and supervised learning settings for most text analysis tasks. With task-specific instruction tuning, Me-LLaMA models also surpass leading commercial LLMs, outperforming ChatGPT on 7 out of 8 datasets and GPT-4 on 5 out of 8 datasets. Moreover, Me-LLaMA's performance is comparable to ChatGPT and GPT-4 for diagnosing complex clinical cases. Our findings underscore combining domain-specific continual pretraining with instruction tuning is essential for developing effective domain-specific large language models in healthcare, significantly enhancing performance across diverse medical text analysis tasks and applications. By publicly releasing our models and resources under appropriate user agreements, we aim to foster innovation and facilitate advancements in medical AI, benefiting researchers and practitioners within the community.