MedExplain is a medical report simplification system that helps patients understand their medical reports in plain language.
Medical reports often contain technical terms, laboratory values, and clinical findings that can be difficult for non-medical users to understand. The aim of this project is to extract important information from a report and present it in a simpler and more understandable way.
Users can upload a medical report, review important findings, explore possible health concerns, retrieve relevant medical knowledge, and ask questions about the report through a conversational assistant.
This project was developed as part of my interest in Artificial Intelligence, Natural Language Processing, and healthcare applications.
https://medexplain-healthcare-ai.streamlit.app/
- Extracts medical values from reports
- Identifies important findings
- Detects abnormal values
- Generates patient-friendly explanations
- Uses semantic analysis instead of relying only on keywords
- Detects symptoms and clinical findings from report text
- Identifies possible health concerns
- Retrieves relevant medical information using FAISS
- Connects report findings with supporting medical knowledge
- Generates contextual explanations
- Ask questions about uploaded reports
- Receive report-specific answers
- Supports follow-up questions
- Uses report context to provide more relevant responses
Generate a downloadable report containing:
- Medical values
- Important findings
- Health concerns
- Simplified explanations
- Recommendations
Supported formats:
- DOCX
- TXT
Scanned medical reports can be processed using OCR.
OCR works best with clear printed reports and may be less reliable for handwritten notes, ECG images, or image-heavy medical documents.
- Streamlit
- Python
- Sentence Transformers
- FAISS
- spaCy
- Semantic NLP techniques
- pdfplumber
- pdf2image
- pytesseract
- python-docx
Medical Report
↓
Text Extraction
↓
Medical Value Extraction
↓
Severity Analysis
↓
Semantic Medical Processing
↓
Medical Knowledge Retrieval
↓
Patient-Friendly Explanation
↓
Conversational Question Answering
↓
PDF Summary Generation
While working with medical reports, I noticed that many people struggle to understand what their reports actually mean.
Most reports are written for healthcare professionals and contain terminology that can be confusing for patients. I wanted to build a system that could make medical information easier to understand while still preserving the important context behind the findings.
This project also allowed me to explore practical applications of Natural Language Processing, semantic search, retrieval systems, and conversational AI in the healthcare domain.
Clone the repository:
git clone https://github.com/rpraneeth63/MedExplain-Healthcare-AI.git
cd MedExplain-Healthcare-AIInstall dependencies:
pip install -r requirements.txtRun the application:
streamlit run app.pyLanguage model files are not included in this repository.
Download a compatible GGUF model and place it inside:
models/
Example:
models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf
MedExplain v1.5
Implemented modules:
- Medical report understanding
- Semantic medical NLP
- FAISS-based retrieval
- Conversational assistant
- PDF export
- Multi-format document support
- OCR support for scanned reports
Some possible future enhancements include:
- ECG interpretation
- Retinal scan analysis
- Prescription understanding
- Multimodal healthcare AI
- Specialist recommendation systems
This project was developed for educational and research purposes.
The information generated by MedExplain should not be considered medical advice, diagnosis, or treatment. Users should always consult a qualified healthcare professional regarding medical concerns.