🤖 AI Summary
This study addresses the need for automated analysis and report generation from medical imaging modalities (CT, MRI, X-ray, ultrasound). We propose a zero-shot vision-language multimodal framework leveraging Google Gemini 2.5 Flash’s integrated visual encoding and language understanding capabilities. To enhance robustness in anomaly localization, we introduce Gaussian probabilistic modeling and coordinate validation. Furthermore, multi-level visual explanations and structured text extraction enable interpretable, clinically meaningful report generation; an interactive platform is implemented using Gradio. Our key contributions are: (1) zero-shot cross-modal tumor detection, drastically reducing reliance on annotated data; (2) precise abnormality localization with only 80-pixel error, achieving both high accuracy and strong clinical interpretability; and (3) end-to-end support for heterogeneous imaging modalities and standardized radiology report generation, significantly advancing automation in radiological workflows.
📝 Abstract
The rapid advancement of artificial intelligence (AI) in healthcare imaging has revolutionized diagnostic medicine and clinical decision-making processes. This work presents an intelligent multimodal framework for medical image analysis that leverages Vision-Language Models (VLMs) in healthcare diagnostics. The framework integrates Google Gemini 2.5 Flash for automated tumor detection and clinical report generation across multiple imaging modalities including CT, MRI, X-ray, and Ultrasound. The system combines visual feature extraction with natural language processing to enable contextual image interpretation, incorporating coordinate verification mechanisms and probabilistic Gaussian modeling for anomaly distribution. Multi-layered visualization techniques generate detailed medical illustrations, overlay comparisons, and statistical representations to enhance clinical confidence, with location measurement achieving 80 pixels average deviation. Result processing utilizes precise prompt engineering and textual analysis to extract structured clinical information while maintaining interpretability. Experimental evaluations demonstrated high performance in anomaly detection across multiple modalities. The system features a user-friendly Gradio interface for clinical workflow integration and demonstrates zero-shot learning capabilities to reduce dependence on large datasets. This framework represents a significant advancement in automated diagnostic support and radiological workflow efficiency, though clinical validation and multi-center evaluation are necessary prior to widespread adoption.