Authors: B. M. Promod Kumar, Bhavana N. S., C. Chinmayi, Deepthi C. Shekar, Deenadayal B. K.
Abstract: Radiology reports generated from imaging modalities such as X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound scans are critical clinical documents. However, these reports are authored in complex medical terminology intended for radiologists and specialist physicians, rendering them largely inaccessible to patients and non-medical users. This communication gap results in confusion, anxiety, and increased dependency on healthcare professionals for basic explanations. This paper presents MedLens, an AI- powered radiology report simplification system that bridges this gap by leveraging Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG). The system extracts text from uploaded PDF reports using PyMuPDF, processes clinical content using Google Gemini AI models, and generates accurate, context-aware patient-friendly summaries. It further classifies the urgency of findings into levels (Low, Moderate, High, Critical), and integrates multilingual translation, text-to-speech functionality, and an AI-powered contextual chatbot. The platform is deployed using FastAPI on the backend and React.js with Tailwind CSS on the frontend. Experimental results demonstrate that MedLens successfully simplifies complex medical terminology, detects critical conditions, provides multilingual support, and enables interactive report-based queries, thereby empowering patients with better health awareness and facilitating informed discussions with healthcare providers.
DOI: https://doi.org/10.5281/zenodo.20351715
