QuarkMed Medical Foundation Model Technical Report

• URL: http://arxiv.org/abs/2508.11894v1
• Date: Sat, 16 Aug 2025 03:47:52 GMT
• Title: QuarkMed Medical Foundation Model Technical Report
• Authors: Ao Li, Bin Yan, Bingfeng Cai, Chenxi Li, Cunzhong Zhao, Fugen Yao, Gaoqiang Liu, Guanjun Jiang, Jian Xu, Liang Dong, Liansheng Sun, Rongshen Zhang, Xiaolei Gui, Xin Liu, Xin Shang, Yao Wu, Yu Cao, Zhenxin Ma, Zhuang Jia,
• Abstract summary: Medical tasks often demand highly specialized knowledge, professional accuracy, and customization capabilities.<n>QuarkMed addresses these needs by leveraging curated medical data processing, medical-content Retrieval-Augmented Generation (RAG), and a large-scale, verifiable reinforcement learning pipeline.<n>The model achieved 70% accuracy on the Chinese Medical Licensing Examination, demonstrating strong generalization across diverse medical benchmarks.
• Score: 14.083189432972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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