Medical text analysis using quantum inspired NLP

Medical reports often contain complex and unstructured textual information that is difficult for both patients and healthcare professionals to analyze efficiently. Manual interpretation of these reports is time-consuming and may lead to overlooked insights. To address this challenge, this mini project proposes a Quantum-Based Medical Text Report Analyzer, an intelligent system designed to automatically analyze and interpret clinical text reports. The proposed system employs Natural Language Processing (NLP) techniques to preprocess medical text, extract key entities such as diseases, symptoms, and medications, and classify reports based on potential health risks. In addition, quantum-inspired computational methods are incorporated to manage uncertainty and complex feature relationships inherent in medical language, thereby improving text representation and enhancing analytical accuracy compared to conventional approaches.The system generates a simplified summary of the medical report, enabling better patient understanding while supporting healthcare professionals in faster and more informed decision-making. This project demonstrates the effective integration of quantum-inspired methods and NLP for scalable, future-ready automated medical text analysis in healthcare applications