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April 27, 20260 citationsOpen Access

AI Driven Visual Question Answering in Healthcare

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DPDr.S.Soundararajan., Vishwashree R, Yasmin J. Department of Computer Science & Engineering Velammal Institute of Technology , PanchettiDTDEPARTMENT OF ARTIFICIAL INTELLIGENCE AND DATA SCIENCE R.M.K. College of Engineering and TechnologyMPMISSILE MAN SCIENTIFIC AND RESEARCH PUBLICATIONS

Key Points

  • This research aims to develop an AI-driven system for analyzing medical images and predicting diseases.
  • Utilized pre-trained Convolutional Neural Network (CNN) models for disease detection from CT and MRI scans.
  • Integrated Large Language Models (LLMs) to provide insights and explanations based on scan results.
  • Created a platform for users to upload images for automated analysis.
  • The system accurately detects diseases such as brain stroke and lung diseases from medical scans.
  • Improved interpretation of scan results and faster decision-making for healthcare professionals.
  • Reduced manual effort in diagnosing conditions, enhancing overall efficiency.

Abstract

Healthcare diagnosis using medical images has become increasingly important with advancements in Artificial Intelligence. This project proposes an intelligent multi- disease analysis system that integrates deep learning models and Large Language Models (LLMs) to analyze medical scan images and predict possible diseases. The system uses pre-trained Convolutional Neural Network (CNN) models to detect diseases such as brain stroke and lung diseases from CT and MRI scans. In addition, a Large Language Model is incorporated to provide descriptive insights and explanations based on the uploaded images. It helps in interpreting scan results, explaining disease characteristics, and offering meaningful medical insights. The proposed system provides a unified platform where users can upload images for automated analysis. By combining image classification with natural language understanding, the system improves diagnostic support, reduces manual effort, and assists healthcare professionals in making faster and more accurate clinical decisions.

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Cite This Study

Panchetti et al. (2026) studied this question.

synapsesocial.com/papers/69eefd9bfede9185760d4573https://doi.org/10.5281/zenodo.19766310
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