The aim is to automate processes such as diagnosing based on patient history, recommending tests, interpreting results, and providing personalized health assessments with the support of artificial intelligence.
The project aims to provide clinical decision support for doctors, offer preliminary information to patients, and reduce the workload within the healthcare system.
In the initial phase of the project, the data collection process has been focused on the field of Internal Medicine; therefore, diseases specific to this area will be prioritized for evaluation. In later stages, data will also be gathered from other medical specialties, aiming to obtain stronger insights into a broader range of health conditions.
Click here to see the sprint-1 workouts
You can run the project by following the steps below:
# 1. Clone repository (optional)
git clone https://github.com/dvaser/Mediary.git
cd Mediary
# 2. Create a virtual environment (.venv)
python -m venv .venv
# 3. Activate the virtual environment (.venv)
# Windows (PowerShell)
.venv\Scripts\Activate.ps1
# If you get a script execution error in PowerShell:
Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
# Windows (CMD)
.venv\Scripts\activate.bat
# Windows (Powershell)
.venv\Scripts\activate
# macOS / Linux
source .venv/bin/activate
# 4. Install the required packages
pip install -r requirements.txt
# 5. Start the application
python main.py
# 6. Log out of the virtual environment (.venv)
deactivate
# (In addition) Transfer libraries used (requirements.txt)
pip freeze > requirements.txtPDFChunker ---> GeminiEmbedder ---> ChromaDBWrapper ---> Query + Gemini Answer
project/
│
├── code/
│ ├── pdf_prep.py # PDFChunker
│ ├── model/
│ │ ├── gemini.py # GeminiEmbedder + GeminiAnswerGenerator
│ │ └── chroma.py # ChromaDBWrapper
│ └── pipeline.py # RAGPipeline
│
├── main.py # Uygulamanın başlangıç noktası
└── chromadb_persist/ # Vektör veritabanı dosyaları (otomatik oluşur)
🧠 AI-Powered Diagnosis Prediction: Predicts possible diseases based on the patient's medical history.
🔬 Test Recommendation System: Lists diagnostic tests relevant to the suspected condition and submits them for physician approval.
📊 Test Result Analysis: Compares results against normal reference ranges and interprets them accordingly.
💬 AI-Assisted Interpretation: Simplifies and summarizes all medical data into clear, understandable reports.
🌐 Web-Based Interface: User-friendly input and output panels designed for both patients and healthcare professionals.
📁 Database and Knowledge Mapping: Includes disease–test associations and reference value tables for clinical accuracy.