Reliable Medical Image Analysis Using Vision - Language Model

Abstract

The increasing use of medical imaging technologies such as CT, MRI, and X-ray has significantly increased the volume of diagnostic data generated in healthcare systems. Radiologists must analyze large numbers of images and produce detailed reports, making the process time-consuming and sometimes inconsistent. Although artificial intelligence has shown promising results in medical image analysis, many existing systems focus mainly on prediction accuracy and often lack reliability, interpretability, and seamless integration with clinical workflows. This research proposes a Reliable Medical Report Analyzer using Visual–Language Models (VLMs) that can jointly understand medical images and textual medical knowledge to support automated report analysis. The proposed framework performs preprocessing and feature extraction from medical images to capture relevant visual patterns. These visual representations are then integrated with language modeling components to enable cross-modal understanding between imaging data and medical terminology. To improve system reliability, uncertainty estimation and calibration techniques are incorporated to assess the confidence of model predictions. The proposed approach aims to assist radiologists by providing consistent and reliable automated analysis, reducing workload and supporting the integration of AI-based tools into real clinical workflows.

Research Team