Recent advancements in large language models have significantly accelerated their adoption in healthcare applications, including AI-powered medical consultations, diagnostic report assistance, and medical search tools. However, medical tasks often demand highly specialized knowledge, professional accuracy, and customization capabilities, necessitating a robust and reliable foundation model. QuarkMed addresses these needs by leveraging curated medical data processing, medical-content Retrieval-Augmented Generation (RAG), and a large-scale, verifiable reinforcement learning pipeline to develop a high-performance medical foundation model. The model achieved 70% accuracy on the Chinese Medical Licensing Examination, demonstrating strong generalization across diverse medical benchmarks. QuarkMed offers a powerful yet versatile personal medical AI solution, already serving over millions of users at ai.quark.cn.
@article{arxiv.2508.11894,
title = {QuarkMed Medical Foundation Model Technical Report},
author = {Ao Li and Bin Yan and Bingfeng Cai and Chenxi Li and Cunzhong Zhao and Fugen Yao and Gaoqiang Liu and Guanjun Jiang and Jian Xu and Liang Dong and Liansheng Sun and Rongshen Zhang and Xiaolei Gui and Xin Liu and Xin Shang and Yao Wu and Yu Cao and Zhenxin Ma and Zhuang Jia},
journal= {arXiv preprint arXiv:2508.11894},
year = {2025}
}