Med.AI was built to solve the "Information Overload" crisis in healthcare by leveraging cutting-edge ML to distill complex documents into actionable intelligence.
Healthcare professionals spend 30% of their time reviewing administrative documents. Policy briefs are dense, verbose, and difficult to parse quickly.
Updated guidelines for rapid assessment. Critical focus on reducing wait times for cardiovascular symptoms.
Users upload PDF/DOCX files. Our system securely parses raw text, maintaining structural integrity via OCR/Text extraction.
The supervised method applies TF-IDF features with Logistic Regression for classification, while the unsupervised method uses TF-IDF with TextRank to identify and rank important content.
A concise summary is generated. The Gemini API then acts as a reasoning engine to answer specific Q&A.
Term Frequency–Inverse Document Frequency. A way to find which words are most important in a text.
A model that learns from examples to decide if something is important or not.
A method that finds the most important sentences by seeing how they connect to each other.
Google's multimodal generative AI, used here for reasoning and Q&A tasks.