While recent advancements in commercial large language models (LM)  have shown promising results in medical tasks, their closed-source nature poses significant privacy and security concerns, hindering their widespread use in the medical field.
Despite efforts to create open-source models, their limited parameters often result in insufficient multi-step reasoning capabilities required for solving complex medical problems.
To address this, we introduce Meerkat-7B, a novel medical AI system with 7 billion parameters.
Meerkat-7B was trained using our new synthetic dataset consisting of high-quality chain-of-thought reasoning paths sourced from 18 medical textbooks, along with diverse instruction-following datasets.
Our system achieved remarkable accuracy across seven medical benchmarks, surpassing GPT-3.5 by 13.1%, as well as outperforming the previous best 7B models such as MediTron-7B and BioMistral-7B by 13.4% and 9.8%, respectively.
Notably, it surpassed the passing threshold of the United States Medical Licensing Examination (USMLE)  for the first time for a 7B-parameter model.
Additionally, our system offered more detailed free-form responses to clinical queries compared to existing 7B and 13B models, approaching the performance level of GPT-3.5.
This significantly narrows the performance gap with large LMs, showcasing its effectiveness in addressing complex medical challenges.