QuarkMed Medical Foundation Model Technical Report
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911107213426688 |
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| author | Li, Ao Yan, Bin Cai, Bingfeng Li, Chenxi Zhao, Cunzhong Yao, Fugen Liu, Gaoqiang Jiang, Guanjun Xu, Jian Dong, Liang Sun, Liansheng Zhang, Rongshen Gui, Xiaolei Liu, Xin Shang, Xin Wu, Yao Cao, Yu Ma, Zhenxin Jia, Zhuang |
| author_facet | Li, Ao Yan, Bin Cai, Bingfeng Li, Chenxi Zhao, Cunzhong Yao, Fugen Liu, Gaoqiang Jiang, Guanjun Xu, Jian Dong, Liang Sun, Liansheng Zhang, Rongshen Gui, Xiaolei Liu, Xin Shang, Xin Wu, Yao Cao, Yu Ma, Zhenxin Jia, Zhuang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11894 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QuarkMed Medical Foundation Model Technical Report Li, Ao Yan, Bin Cai, Bingfeng Li, Chenxi Zhao, Cunzhong Yao, Fugen Liu, Gaoqiang Jiang, Guanjun Xu, Jian Dong, Liang Sun, Liansheng Zhang, Rongshen Gui, Xiaolei Liu, Xin Shang, Xin Wu, Yao Cao, Yu Ma, Zhenxin Jia, Zhuang Artificial Intelligence 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. |
| title | QuarkMed Medical Foundation Model Technical Report |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.11894 |