| # Citrus: Leveraging Expert Cognitive Pathways in a Medical Language Model for Advanced Medical Decision Support | |
| <p align="center"> | |
| <a href="https://arxiv.org/abs/2502.18274" target="_blank">📑Paper</a> ｜<a href="https://jdh-algo.github.io/Citrus/" target="_blank">🤗Github Page</a> ｜<a href="https://huggingface.co/jdh-algo/Citrus1.0-llama-70B" target="_blank">🤗Model</a> ｜<a href="https://huggingface.co/datasets/jdh-algo/Citrus_S3" target="_blank">📚Medical Reasoning Data</a> ｜ <a href="https://huggingface.co/datasets/jdh-algo/JMED" target="_blank">📚Evaluation Data</a> | |
| </p> | |
| ## The Introduction to Our Work | |
| ### 1. Main approaches | |
| <div align="center"> | |
| <img src="https://raw.githubusercontent.com/jdh-algo/Citrus/main/static/images/figure4-1-2.png" alt="image" width="75%"/> | |
| </div> | |
| ### 2. Overview of training stages and training data pipeline | |
| <div align="center"> | |
| <img src="https://raw.githubusercontent.com/jdh-algo/Citrus/main/static/images/figure4-2-1.png" width="75%"> | |
| </div> | |
| Citrus is a medical language model that bridges the gap between clinical expertise and AI reasoning by emulating the cognitive processes of medical experts. The model is trained on a large corpus of simulated expert disease reasoning data in sft-stage-3, synthesized using a novel approach that accurately captures the decision-making pathways of clinicians. | |
| The contributions of this work are as follows: | |
| 1. We propose a training-free reasoning approach that emulates the cognitive processes of medical experts, enabling large language models to enhance their medical capabilities in clinical diagnosis and treatment. | |
| 2. In conjunction with the data construction method, we introduce a multi-stage post-training approach to further improve the model’s medical performance. | |
| 3. We have made the Citrus model and its training data publicly available as open-source resources to advance research in AI-driven medical decision-making. | |
| 4. We have developed and open-sourced a large-scale, updatable clinical practice evaluation dataset based on real-world data, accurately reflecting the distribution of patients in real-world settings. | |
| ## Notice | |
| 1. Our model is built with Qwen2.5-72B, Qwen is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved. | |
| 2. Our default license is MIT, provided it does not conflict with the Qwen LICENSE. |