MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs
Baorong Shi Bo Cui Boyuan Jiang Deli Yu Fang Qian Haihua Yang Huichao Wang Xu Wang Yijun He Zhixiong Yang Jiale Chen Jianfei Pan Jieqiong Cao Jinghao Lin Kai Wu Lin Yang Shengsheng Yao Tao Chen Xiaojun Xiao Xiaozhong Ji
作者信息
Abstract
We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world clinical applications. MedXIAOHE achieves state-of-the-art performance across diverse medical benchmarks and surpasses leading closed-source multimodal systems on multiple capabilities. To achieve this, we propose an entity-aware continual pretraining framework that organizes heterogeneous medical corpora to broaden knowledge coverage and reduce long-tail gaps (e.g., rare diseases). For medical expert-level reasoning and interaction, MedXIAOHE incorporates diverse medical reasoning patterns via reinforcement learning and tool-augmented agentic training, enabling multi-step diagnostic reasoning with verifiable decision traces. To improve reliability in real-world use, MedXIAOHE integrates user-preference rubrics, evidence-grounded reasoning, and low-hallucination long-form report generation, with improved adherence to medical instructions. We release this report to document our practical design choices, scaling insights, and evaluation framework, hoping to inspire further research.引用本文复制引用
Baorong Shi,Bo Cui,Boyuan Jiang,Deli Yu,Fang Qian,Haihua Yang,Huichao Wang,Xu Wang,Yijun He,Zhixiong Yang,Jiale Chen,Jianfei Pan,Jieqiong Cao,Jinghao Lin,Kai Wu,Lin Yang,Shengsheng Yao,Tao Chen,Xiaojun Xiao,Xiaozhong Ji.MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs[EB/OL].(2026-02-16)[2026-02-19].https://arxiv.org/abs/2602.12705.学科分类
医学现状、医学发展/医学研究方法/临床医学
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