Medical Image Analysis With AI Assist

Authors: Mynam Hemanth Kumar, Koparthi Mahendra, Koppala Vijay Raju, Ms.G.Archana

Abstract: The proliferation of digital medical imaging has created an urgent need for automated, accessible tools capable of performing image segmentation and clinical interpretation without requiring specialist infrastructure. This paper presents Medical Image Analysis with AI Assist, a web-based medical imaging platform developed in Python and Streamlit. The system integrates a dual-pathway segmentation architecture — a primary UNet/DOSMA pipeline for DICOM/NIfTI data and a CLAHE-Otsu-morphological fallback for standard formats — with pixel-level statistical analysis, Google Gemini 1.5 Flash-powered clinical summarisation, and a stateful conversational chatbot. Evaluation on 150 de-identified images across five modalities yields: segmentation coverage accuracy 91.4%, expert-rated AI summary quality 4.2/5.0 (Krippendorff's α = 0.74, three raters), chatbot relevance 4.2/5.0, and mean processing time 16.8 s. CLAHE parameters (clip limit 2.5, 8×8 tile) are empirically justified; evaluation methodology, report completeness measurement, comparative fairness, patient privacy, data governance, clinical safety, hardware specification, and state-of-the-art benchmarking are explicitly addressed throughout.