  Large-scale language models (LLMs) like ChatGPT have demonstrated impressive
abilities in generating responses based on human instructions. However, their
use in the medical field can be challenging due to their lack of specific,
in-depth knowledge. In this study, we present a system called LLMs Augmented
with Medical Textbooks (LLM-AMT) designed to enhance the proficiency of LLMs in
specialized domains. LLM-AMT integrates authoritative medical textbooks into
the LLMs' framework using plug-and-play modules. These modules include a Query
Augmenter, a Hybrid Textbook Retriever, and a Knowledge Self-Refiner. Together,
they incorporate authoritative medical knowledge. Additionally, an LLM Reader
aids in contextual understanding. Our experimental results on three medical QA
tasks demonstrate that LLMAMT significantly improves response quality, with
accuracy gains ranging from 11.6% to 16.6%. Notably, with GPT-4-Turbo as the
base model, LLM-AMT outperforms the specialized Med-PaLM 2 model pre-trained on
a massive amount of medical corpus by 2-3%. We found that despite being 100x
smaller in size, medical textbooks as a retrieval corpus is proven to be a more
effective knowledge database than Wikipedia in the medical domain, boosting
performance by 7.8%-13.7%.
