International Journal of Science, Engineering and TechnologyAuthors: Devansh, Darshit Chauhan, Aditya Singh, Mr. Anurag Chandana
Abstract: The healthcare industry generates enormous volumes of clinical data daily, including patient histories, diagnostic results, lab reports, and physician notes. Converting this unstructured data into structured, readable medical reports and discharge summaries is a time-consuming and error-prone process when done manually. This research presents a GEN-AI driven system for automated medical report generation and discharge summary creation using large language models (LLMs), machine learning (ML) algorithms, and natural language processing (NLP) techniques. The system accepts raw clinical inputs including patient demographics, symptoms, diagnosis codes, prescribed medications, and lab results and automatically generates structured medical reports and professional discharge summaries. The project utilizes a web-based interface developed with modern frontend technologies and integrates backend ML pipelines for intelligent content generation. The results demonstrate significant improvements in documentation speed, accuracy, and consistency compared to manual approaches.
