ReasonMed is currently the largest open source medical reasoning dataset, and the related paper results are:ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning", which aims to train and evaluate models for tasks such as medical question answering and text generation. The dataset contains 370,000 high-quality question-answering examples, covering multiple fields such as clinical knowledge, anatomy, genetics, etc. The data is extracted from 1.75 million initial reasoning paths generated by three large language models (Qwen-2.5-72B, DeepSeek-R1-Distill-Llama-70B, and HuatuoGPT-o1-70B), and is refined through a rigorous multi-agent validation and optimization process.

Citation

@misc{sun2025reasonmed370kmultiagentgenerated,
title={ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning},
author={Yu Sun and Xingyu Qian and Weiwen Xu and Hao Zhang and Chenghao Xiao and Long Li and Yu Rong and Wenbing Huang and Qifeng Bai and Tingyang Xu},
year={2025},
eprint={2506.09513},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.09513},
}
@misc{lasateam2025lingshugeneralistfoundationmodel,
title={Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning},
author={LASA Team and Weiwen Xu and Hou Pong Chan and Long Li and Mahani Aljunied and Ruifeng Yuan and Jianyu Wang and Chenghao Xiao and Guizhen Chen and Chaoqun Liu and Zhaodonghui Li and Yu Sun and Junao Shen and Chaojun Wang and Jie Tan and Deli Zhao and Tingyang Xu and Hao Zhang and Yu Rong},
year={2025},
eprint={2506.07044},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.07044},
}