MeddAI - Medical Content Creator AI Web Application
Medd AI Web App
A web app that drafts compliant medical content tuned to brand voice, with built-in approval and publishing workflows.
MeddAI is an AI-powered web application built to generate trustworthy, citation-ready medical content for healthcare professionals, students, and the public. It uses a FastAPI backend with Python, orchestrates multiple AI agents using LangGraph, and integrates Google Gemini and OpenAI GPT-4o for content generation and validation. Perplexity AI is used for research when no external references are provided.
The platform accepts inputs like URLs, DOCX/PDF files, and YouTube videos. It performs real-time source extraction, automated medical accuracy checks, and delivers AMA-style formatted articles. A REST API interface allows seamless content requests by both technical and non-technical users.
- AI-powered medical content generation
- Citation-ready healthcare articles
- FastAPI backend
- LangGraph AI orchestration
- Google Gemini integration
- OpenAI GPT-4o validation
- AMA formatting
- Medical accuracy verification
- Multi-source ingestion (URL, PDF, DOCX, video)
- REST API healthcare
Solving real problems with smart engineering
Validating Medical Accuracy
Problem
Risk of generating inaccurate or non-compliant content that could mislead healthcare audiences.
Solution
Implemented GPT-4o for post-generation validation - checking factual accuracy, AMA citation format, and readability. Invalid drafts are automatically flagged and returned with issue logs.
Ingesting Multiple Content Types
Problem
Required support for diverse sources like PDFs, DOCX, web pages, and YouTube videos in a single pipeline.
Solution
Integrated Firecrawl for web pages, PyPDF2 / python-docx for documents, and RapidAPI for YouTube transcription - seamless ingestion and parsing across formats.
Dynamic Audience Adaptation
Problem
One-size-fits-all tone fails across user groups - doctors, students, patients, and the public all need different language.
Solution
Language generation dynamically adjusts tone and jargon to fit doctors, students, patients, or the general public based on the request context.
Citation-Enforced Content
Problem
Generated content lacked scholarly reference formatting - making it unusable for evidence-based publication.
Solution
Google Gemini is instructed with strict prompting to embed in-text citations and meet academic AMA formatting standards.
A look inside
Built with
Frontend
- Any client (React.js, Postman, etc.) - sends requests and renders article output
- REST endpoints designed for easy integration into existing UIs
Backend
- FastAPI - endpoints for article generation, extraction, and health checks
- LangGraph - orchestrates the multi-step workflow (input → research → generation → validation)
- Python - core business logic and integrations
AI Models & External APIs
- Google Gemini - generates draft content with strict citation prompting
- OpenAI GPT-4o - validates factual accuracy and formatting compliance
- Perplexity AI - literature search fallback when no source is provided
- Firecrawl, PyPDF2 / python-docx, RapidAPI - multi-format ingestion
Recommendation Engine & Security
- Auto-routes requests to the right workflow based on input type (URL, PDF, DOCX, video)
- Word-count accuracy within ±10% via Gemini constraints
- Asynchronous validation and rate-limiting for abuse protection
- API keys behind secure access layers; HIPAA-conscious practices
What actually changed
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