Semantic Understanding And Contextual Explanation of Medical Documents

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