https://github.com/freedomintelligence/easymed
https://github.com/freedomintelligence/easymed
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/freedomintelligence/easymed
- Owner: FreedomIntelligence
- Created: 2025-06-12T04:17:16.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-06-29T07:33:26.000Z (11 months ago)
- Last Synced: 2025-06-29T07:36:39.080Z (11 months ago)
- Size: 299 KB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🏥 EasyMED: AI-Powered Clinical Skills Training Platform
An advanced AI tutor designed to revolutionize medical education by simulating realistic, standardized patient interactions.
Overview •
Features •
Research & Experiments •
How It Works •
Contributing •
Citation
---
## 🎯 Overview
**EasyMED** is a Virtual Standardized Patient (VSP) system powered by Large Language Models (LLMs). It is designed to address the core challenges of traditional medical training using human Standardized Patients (SPs), namely **high costs, poor scalability, and a lack of consistency**.
Our platform provides medical students with a safe and repeatable environment to practice a full range of core clinical skills by interacting with highly realistic AI patients. This includes **clinical consultation, physical examination, ordering ancillary tests, making a diagnosis, and formulating a treatment plan**.
More than just a simulator, EasyMED is an intelligent tutor. Its built-in **Clinical Reasoning Path Tracing and Evaluation (CR-PTE)** framework automatically assesses student performance, providing instant, objective, and data-driven feedback to help them efficiently improve their clinical reasoning skills.
---
## ✨ Features
- 💬 **Natural Language Consultation:** Engage in fluid, medically logical conversations with AI patients.
- 🩺 **Multi-Scenario Clinical Simulation:** Covers the five core stages of a clinical encounter, from consultation to treatment.
- 🔬 **Dynamic Lab & Imaging Generation:** Order tests like a complete blood count (CBC) or X-rays, and the system will generate dynamic reports that match the case's pathophysiology.
- 🤖 **Intelligent Assessment & Feedback:** The built-in CR-PTE framework automatically analyzes and scores a student's clinical reasoning path.
- 📈 **Data-Driven Educational Analytics:** Logs and analyzes learning behaviors to provide educators with insights for curriculum optimization.
---
## 🧪 Research & Experiments
To comprehensively evaluate the value of EasyMED, we designed and implemented a single, integrated study. This rigorous controlled experiment aims to simultaneously answer key questions regarding the system's **Efficacy**, **Realism**, and **Reliability**.
> **Core Research Question:** Compared to traditional human SPs, can the LLM-driven EasyMED serve as a more effective, reliable, and well-received alternative for clinical skills training?
### **Experimental Design & Procedure**
To most effectively compare the two training methods, we employed a rigorous **Two-Period Crossover Design**. This design allows each participant to experience both the EasyMED and human SP modalities, thereby serving as their own control and making the results more reliable.
* **Participants & Baseline:**
* We recruited **20 medical students** who had completed their theoretical coursework but had not yet passed the national clinical skills examination.
* Prior to the experiment, all participants took a **Baseline Pre-test** to assess their initial skill level.
* Based on the pre-test scores, participants were randomly assigned to two balanced groups using a matched-pairs methodology: Group A (n=10) and Group B (n=10).
* **Two-Period Crossover Procedure:**
The entire experiment lasted four weeks and was divided into two periods. A comprehensive skills assessment was conducted after each period.
| Period | Duration | Group A (n=10) Training Method | Group B (n=10) Training Method |
| :--- | :--- | :--- | :--- |
| **Period 1** | First 2 Weeks | 🤖 **Trains with the EasyMED System** | 🧑⚕️ **Trains with a Human SP** |
| *Mid-Experiment Test* | End of Week 2 | \- | *All 20 participants take the first clinical skills assessment* |
| **Period 2** | Last 2 Weeks | 🧑⚕️ **Trains with a Human SP** (Crossover) | 🤖 **Trains with the EasyMED System** (Crossover) |
| *Final Test* | End of Week 4 | \- | *All 20 participants take the final clinical skills assessment* |
* **Blinded Assessment:**
* All mid-experiment and final skills assessments were scored by external expert examiners who were **blinded to the training modality each student received** during the respective period. This ensures the absolute objectivity and fairness of the evaluation.
### **Multi-Faceted Data Analysis**
After the experiment, we conducted an in-depth analysis of the rich data we collected across three key dimensions:
#### **1. Efficacy Analysis**
* **Objective:** To determine the actual effectiveness of EasyMED in improving the clinical skills of medical students.
* **Methods:**
* **Primary Metric:** Compared the **Gain Score (Post-test - Pre-test)** on clinical skills assessments between the two groups.
* **Statistical Test:** Used an independent samples t-test to analyze if the difference between groups was statistically significant.
* **Subjective Feedback:** Analyzed changes in students' self-reported confidence from pre- and post-experiment questionnaires.
#### **2. Realism & Consistency Analysis**
* **Objective:** To assess how closely the EasyMED VSP's conversational behavior resembles that of a human SP.
* **Methods:**
* Dialogue logs from both Group A (interacting with VSP) and Group B (interacting with human SP) were extracted.
* **Semantic Consistency:** Used models like Sentence-BERT to calculate the cosine similarity between VSP and human SP responses in the same context.
* **Interaction Patterns:** Compared the average **Interaction Turns** and **Response Length** to analyze similarities in conversational rhythm and information density.
#### **3. Reliability Analysis of Internal Assessment**
* **Objective:** To validate the accuracy and reliability of the built-in CR-PTE automated assessment framework.
* **Methods:**
* Students' performance data was scored by both the **EasyMED system** and by **external human experts**.
* **Correlation Analysis:** Calculated the **Pearson correlation coefficient** between the system's automated scores and the experts' scores.
* **Consistency Check:** Analyzed the scoring agreement on sub-dimensions such as "history-taking completeness" and "diagnostic accuracy."
---
## 🤝 Contributing
We warmly welcome and appreciate all forms of contributions! Whether you are a developer, a researcher, or a clinical expert, if you have ideas for improving this project or want to fix a bug, please feel free to:
1. **Fork** this repository.
2. Create your feature branch (`git checkout -b feature/AmazingFeature`).
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`).
4. Push to the branch (`git push origin feature/AmazingFeature`).
5. Open a **Pull Request**.
---
## 📜 License
This project is licensed under the [MIT License](LICENSE.txt).