QuarkMed Medical Foundation Model Technical Report
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

TL;DR
QuarkMed is a high-performance medical foundation model that leverages curated data, retrieval-augmented generation, and reinforcement learning to achieve strong accuracy and generalization in healthcare applications.
Contribution
The paper introduces QuarkMed, a novel medical foundation model utilizing medical data processing, RAG, and reinforcement learning for improved healthcare AI performance.
Findings
Achieved 70% accuracy on Chinese Medical Licensing Examination
Demonstrated strong generalization across diverse medical benchmarks
Serves over millions of users as a versatile medical AI solution
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…
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Taxonomy
TopicsMachine Learning in Healthcare · Artificial Intelligence in Healthcare and Education · Domain Adaptation and Few-Shot Learning
