MediSmart leverages machine learning to automate patient triage, optimize doctor schedules, and provide predictive health analytics.
Powering the next generation of clinical management
Utilizes Natural Language Processing (NLP) to categorize patient urgency and match symptoms to relevant medical ICD-10 codes automatically.
Predictive algorithms analyze doctor workload and patient flow to prevent bottlenecking, reducing average wait times by up to 40%.
End-to-end encrypted storage for Electronic Health Records (EHR) featuring automated summarization for rapid doctor review sessions.
Your health path, optimized by intelligence
Input your concerns into our NLP-powered interface. Our AI understands natural human language.
The system cross-references symptoms with medical databases to suggest the most relevant specialist.
Confirm your slot with one click. Automated sync with doctor calendars ensures zero wait time.
Join the 50+ medical institutions already utilizing MediSmart AI to eliminate wait times and improve patient diagnostic accuracy.
Our proprietary NLP engine doesn't just recognize keywords; it understands medical intent. It cross-references patient input with a vector database of over 10,000 symptoms.
Predictive triage logic.
Encrypted data flow.
MediBot v2.0
AI Diagnostic Assistant
Verified reviews from our healthcare community
"The AI triage accuracy is impressive. It correctly identified my chronic migraine symptoms and scheduled me with a neurologist within minutes."
Rahul Sharma
Verified Patient
"This system has transformed my clinic. The automated EHR summaries allow me to focus entirely on the patient rather than digging through old files."
Dr. Ananya Singh
Chief Cardiologist
"The predictive scheduling effectively eliminated our 'no-show' problem. It's a game-changer for hospital administration."
Dr. Michael K.
Hospital Admin
The backend uses a combination of Random Forest Classification and NLP vectorization. It was trained on the UCI Medical dataset to identify 40+ common conditions with high precision.
While this is a major project, the architecture follows HIPAA guidelines using 256-bit AES encryption for data at rest and TLS 1.3 for data in transit through our PHP/MySQL backend.