  Recent advances in large language models (LLMs) have demonstrated exceptional
performance in various natural language processing (NLP) tasks. However, their
effective application in the medical domain is hampered by a lack of medical
domain knowledge. In this study, we present SA-MDKIF, a scalable and adaptable
framework that aims to inject medical knowledge into general-purpose LLMs
through instruction tuning, thereby enabling adaptability for various
downstream tasks. SA-MDKIF consists of two stages: skill training and skill
adaptation. In the first stage, we define 12 basic medical skills and use
AdaLoRA to train these skills based on uniformly formatted instructional
datasets that we have constructed. In the next stage, we train the skill router
using task-specific downstream data and use this router to integrate the
acquired skills with LLMs during inference. Experimental results on 9 different
medical tasks show that SA-MDKIF improves performance by 10-20% compared to the
original LLMs. Notably, this improvement is particularly pronounced for unseen
medical tasks, showing an improvement of up to 30%.
