Semantic Understanding And Contextual Explanation of Medical Documents
Ch Mahender Reddy, K Sai Varun Reddy, M Uday Sreechand, TN Sai Ram, T Ajith Karthikeya
This study introduces an AI-driven system designed to efficiently extract and summarize medical
documents using Optical Character Recognition (OCR) and Natural Language Processing (NLP).
By leveraging TesseractOCR, the system accurately retrieves textual data from diagnostic reports.
The extracted content is then processed through a GPT-based API to generate brief, patientfriendly summaries in 2-3 lines. By simplifying complex medical terms, the system enhances patient
understanding, supports informed decision-making, and improves communication between healthcare
professionals and patients. This method seeks to bridge the gap between technical medical reports
and patient awareness, ultimately contributing to better healthcare outcomes. The implementation is
conducted on Google Colab, ensuring cloud-based execution for improved scalability and accessibility.
