QuarkMed Medical Foundation Model Technical Report

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 users at ai.quark.cn.