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index.html
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<!DOCTYPE html>
<html>
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<script src="static/js/index.js"></script>
<style>
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width: auto; /* 设置表格宽度为75% */
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<body>
<div id="nav-wrapper">
<div id="project-nav-container">
<a href="https://github.com/jdh-algo" title="GitHub主页" class="home-link">
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<div class="select-wrapper">
<select id="project-nav" onchange="location = this.value;">
<option value="">More Research</option>
<option value="https://jdh-algo.github.io/Citrus/">Citrus</option>
</select>
</div>
</div>
</div>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-3 publication-title">Citrus: Leveraging Expert Cognitive Pathways in a Medical Language Model for Advanced Medical Decision Support</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a target="_blank">Guoxin Wang</a><sup>1*</sup>,</span>
<span class="author-block">
<a target="_blank">Minyu Gao</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Shuai Yang</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Ya Zhang</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Lizhi He</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Liang Huang</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Hanlin Xiao</a><sup>1†</sup>,</span>
<span class="author-block">
<a target="_blank">Yexuan Zhang</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Wanyue Li</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Lu Chen</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Jintao Fei</a><sup>1</sup>,</span>
<span class="author-block">
<a target="_blank">Xin Li</a><sup>1</sup></span>
</div>
<div class="is-size-5 publication-authors">
<sup>1</sup> <span class="author-block">Citrus Team, JD Health International Inc</span> <br>
<sup>*</sup> <span class="author-block">Project Lead</span> <br>
<sup>†</sup> <span class="author-block">Work done during the internship at Citrus Team.</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<span class="link-block">
<a href="https://arxiv.org/abs/2502.18274" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a href="https://arxiv.org/abs/2502.18274" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a href="https://github.com/jdh-algo/Citrus" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- huggingface model Link -->
<span class="link-block">
<a href="https://huggingface.co/jdh-algo/Citrus1.0-llama-70B" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<img src="./static/images/artificial-intelligence.png">
</span>
<span>Model</span>
</a>
</span>
<!-- huggingface model Link -->
<span class="link-block">
<a href="https://huggingface.co/datasets/jdh-algo/Citrus_S3" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<img src="./static/images/database.png">
</span>
<span>Medical Reasoning Data</span>
</a>
</span>
<!-- huggingface model Link -->
<span class="link-block">
<a href="https://huggingface.co/datasets/jdh-algo/JMED" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<img src="./static/images/database.png">
</span>
<span>Evaluation Data</span>
</a>
</span>
<!-- huggingface demo Link
<span class="link-block">
<a href="https://huggingface.co/spaces/jdh-algo/JoyHallo" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<img src="./static/images/hf-logo.png">
</span>
<span>Gradio</span>
</a>
</span> -->
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section" style="margin-bottom: 10px; padding: 5px;">
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-4" style="text-align: left;" >Abstract</h2>
<div class="content has-text-justified">
<p>
Large language models (LLMs), particularly those with reasoning capabilities, have rapidly advanced in recent years, demonstrating significant potential across a wide range of applications. However, their deployment in healthcare, especially in disease reasoning tasks, is hindered by the challenge of acquiring expert-level cognitive data. In this paper, we introduce Citrus, 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, synthesized using a novel approach that accurately captures the decision-making pathways of clinicians. This approach enables Citrus to better simulate the complex reasoning processes involved in diagnosing and treating medical conditions.To further address the lack of publicly available datasets for medical reasoning tasks, we release the last-stage training data, including a custom-built medical diagnostic dialogue dataset. This open-source contribution aims to support further research and development in the field. Evaluations using authoritative benchmarks such as MedQA, covering tasks in medical reasoning and language understanding, show that Citrus achieves superior performance compared to other models of similar size. These results highlight Citrus’s potential to significantly enhance medical decision support systems, providing a more accurate and efficient tool for clinical decision-making.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<section class="section" style="margin-bottom: 10px; padding: 5px;">
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-4" style="text-align: left;" >Contributions</h2>
<div class="content has-text-justified">
<p>
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.
</p>
<p>
2. In conjunction with the data construction method, we introduce a multi-stage post-training approach to further improve the model’s medical performance.
</p>
<p>
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.
</p>
<p>
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.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- Paper Method -->
<section class="section" style="margin-bottom: 10px; padding: 5px;">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<!-- <h2 class="title is-4" style="text-align: left;">Method</h2> -->
<!-- <h3 class="title is-5" style="text-align: left;">模型训练的主要细节</h3> -->
<!-- <div class="content has-text-justified">
<p>
模型训练的主要细节
</p>
</div> -->
<br>
<img src="./static/images/雷达图.png" style="display: block; margin: 0 auto; width: 100%; max-width: 100%; height: auto;">
<div class="content has-text-justified">
<p>
<!-- continue -->
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section" style="margin-bottom: 10px; padding: 5px;">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-4" style="text-align: left;">Model Access</h2>
<div class="content has-text-justified">
<table>
<thead>
<tr>
<th>Model Name</th>
<th>Backbone</th>
<th>Link</th>
</tr>
</thead>
<tbody>
<!-- First Method -->
<tr>
<td>Citrus1.0-llama-70B</td>
<td>llama-70B</td>
<td><a href="https://huggingface.co/jdh-algo/Citrus1.0-llama-70B" target="_blank">Model Link</a></td>
</tr>
<!-- Second Method -->
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<td>Citrus1.0-Qwen-72B</td>
<td>Qwen-72B</td>
<td><a href="https://huggingface.co/jdh-algo/Citrus1.0-Qwen-72B" target="_blank">Model Link</a></td>
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<h2 class="title is-4" style="text-align: left;">Data Access</h2>
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<th>Dataset</th>
<th>Dataset Usage</th>
<th>Dataset Description</th>
<th>Link</th>
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<td>Citrus_S3</td>
<td>Train Data</td>
<td>A portion of the training data for the model includes 20k data points.</td>
<td><a href="https://huggingface.co/datasets/jdh-algo/Citrus_S3" target="_blank">Data Link</a></td>
</tr>
<tr>
<td>JMED</td>
<td>Test Data</td>
<td>The dataset originates from anonymized doctor-patient dialogues at JD Health Internet Hospital, filtered to retain consultations adhering to standardized diagnostic workflows. The initial release contains 1,000 high-quality clinical records spanning all age groups (0-90 years) and multiple specialties.</td>
<td><a href="https://example.com/data-link" target="_blank">Data Link</a></td>
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<h2 class="title is-4" style="text-align: left;">Method</h2>
<h3 class="title is-5" style="text-align: left;">1. Main approaches</h3>
<div class="content has-text-justified">
<p>
LLMs preforms similar cognitive pathways as medical experts.CPT enabled LLMs to learn medical knowledge and perform pattern recognition as doctors do, meanwhile LLMs are capable to handle hypothetical-deductive reasoning by executing several specific reasoning steps, which can be trained through SFT and RL procedure
</p>
</div>
<br>
<img src="./static/images/figure4-1-2.png" style="display: block; margin: 0 auto; width: 100%; max-width: 100%; height: auto;">
<br>
<h3 class="title is-5" style="text-align: left;">2. Overview of training stages and training data pipeline</h3>
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The training process consists of three stages: CPT, SFT, and RL. We shows training purposes and dataset scale on each stage, also, we points out the data pipeline in corresponding stage
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<img src="./static/images/figure4-2-1.png" style="display: block; margin: 0 auto; width: 100%; max-width: 100%; height: auto;">
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<h2 class="title is-4" style="text-align: left;">Results</h2>
<h3 class="title is-5" style="text-align: left;">1. Main Results on Medical Benchmarks</h3>
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<p>
<!-- 主要结果 -->
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<img src="./static/images/main_results.png" style="display: block; margin: 0 auto; width: 100%; max-width: 100%; height: auto;">
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<h3 class="title is-5" style="text-align: left;">2. The experiments on Citrus1.0-Llama-70B</h3>
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<p>
<!-- The SFT stage results and RL stage results are shown in sequence to show different contributions to final perform of model from different stages. We also implemented several experiments to reveal the impact from data size and data portion. "w/o" and "w/" denote "without" and "with". Bold highlights the best scores in each segment.Use MedQA benchmark to evaluate the influence on different training stages and data sizes. -->
</p>
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<!-- Paper Experiment -->
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<!-- BibTex citation -->
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<section class="section" id="BibTeX">
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<h2 class="title is-4" >BibTeX</h2>
<!-- <div class="content has-text-justified"> -->
<pre class="has-text-justified" style="margin-bottom: 5px; margin-top: 5px; padding: 5px;">
<code>
@misc{wang2025citrus,
title={Citrus: Leveraging Expert Cognitive Pathways in a Medical Language Model for Advanced Medical Decision Support},
author={Guoxin Wang and Minyu Gao and Shuai Yang and Ya Zhang and Lizhi He and Liang Huang and Hanlin Xiao and Yexuan Zhang and Wanyue Li and Lu Chen and Jintao Fei and Xin Li},
year={2025},
eprint={2502.18274},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2502.18274}, }
</code>
</pre>
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