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| title: MediSim | |
| emoji: "🩺" | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: false | |
| # MediSim: Multimodal Diagnostic and Agentic Triage System | |
| MediSim is an AI-powered medical assistant web application designed to safely process health inputs. It is developed as an NLP research project focused on reducing clinical hallucination in generative healthcare applications using hybrid learning pipelines and multi-agent orchestration. | |
| ## Core Features | |
| ### 1. Multimodal Diagnostic Assistant | |
| - Purpose: Provides preliminary diagnostic assessments by combining medical image data and symptom descriptions. | |
| - Input: Medical scans (for example, chest X-ray) plus symptom text. | |
| - Architecture: | |
| - Vision encoder: ResNet-18. | |
| - Text encoder: biLSTM. | |
| - Fusion head: late-fusion classifier. | |
| - Advantage: Better reliability and lower compute demands than large generic multimodal models in this domain. | |
| ### 2. Agentic Triage and Consultation | |
| - Purpose: Interactively gathers symptoms and provides verified clinical guidance. | |
| - Processing: Three-agent collaboration loop: | |
| - Triage Nurse: empathic intake and symptom collection. | |
| - Specialist Doctor: differential reasoning and next-step planning. | |
| - Fact Checker: verifies outputs against safety constraints. | |
| - Advantage: Reduces hallucination risk through explicit multi-agent verification. | |
| ## Architecture | |
| - Frontend: React + TypeScript + Vite. | |
| - Backend: FastAPI + PyTorch + LangChain orchestration. | |
| - Authentication and Storage: Firebase Auth + Firestore. | |
| - Deployment target: Hugging Face Space (Docker). | |
| ## Directory Layout | |
| ```text | |
| MediSim/ | |
| |- web_app_pro/ # Production web application | |
| | |- frontend/ # React + Vite app | |
| | |- backend/ # FastAPI service and model logic | |
| |- web_app/ # Legacy app entrypoint used for HF runtime | |
| |- data/ # Trained weights and supporting assets | |
| |- notebooks/ # Training and experimentation notebooks | |
| |- reports/ # Project reports and writeups | |
| |- scripts/ # Deployment and utility scripts | |
| `- README.md | |
| ``` | |
| ## Local Development | |
| ### Backend | |
| ```bash | |
| cd web_app_pro/backend | |
| pip install -r requirements.txt | |
| python main.py | |
| ``` | |
| ### Frontend | |
| ```bash | |
| cd web_app_pro/frontend | |
| npm install | |
| npm run dev | |
| ``` | |
| ## Hugging Face Deployment Notes | |
| This repository includes: | |
| - A Docker-based Space configuration. | |
| - Space runtime entrypoint through `web_app/app.py`. | |
| - Environment-driven Firebase and backend configuration. | |
| ## Team Members | |
| - Htut Ko Ko (st126010) | |
| - Imtiaz Ahmad (st126685) | |
| - Michael R. Lacar (st126161) | |
| - Aashutosh Raut (st126438) | |
| ## References | |
| See project reports under `reports/` for methodology, literature review, and evaluation details. | |