AI-Powered Medical Text Summarization
A project by Aryan Pathak · The Neural Guy
Hybrid Transformer-BiLSTM model for automated medical Q&A summarization with dual outputs for clinicians and patients.
The Problem
Medical question-answer data is often lengthy and difficult for both patients and healthcare professionals to quickly interpret.
The Solution
Developed a hybrid deep learning architecture combining 8-head attention Transformers with BiLSTM and knowledge graphs to generate concise, context-aware summaries.
Architecture
RESTful Flask API serving a dual-summary generation pipeline with role-specific output formatting.
Role
AI/ML Engineer
Impact
Achieved 88%+ summarization accuracy with optimized inference time between 5–15 seconds under constrained hosting environments.
Lessons I Learned
Careful model optimization and attention tuning significantly improve contextual accuracy in domain-specific NLP tasks.