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.

翻译：人工智能在医疗影像领域的快速发展已彻底革新了诊断医学与临床决策流程。本研究提出一种用于医学图像分析的智能多模态框架，该框架在医疗诊断中利用了视觉语言模型。该框架集成Google Gemini 2.5 Flash，实现了跨CT、MRI、X射线和超声等多种成像模态的自动化肿瘤检测与临床报告生成。系统通过结合视觉特征提取与自然语言处理技术，实现了上下文感知的图像解析，并融合了坐标验证机制与用于异常分布建模的概率高斯模型。多层可视化技术可生成详细的医学图示、叠加对比图像及统计表征图以提升临床置信度，其位置测量平均偏差达到80像素。结果处理环节采用精密的提示工程与文本分析技术来提取结构化临床信息，同时保持模型的可解释性。实验评估表明系统在多模态异常检测任务中表现出优异性能。该系统配备用户友好的Gradio界面以便临床工作流集成，并展现出零样本学习能力以降低对大规模数据集的依赖。该框架标志着自动化诊断支持与放射工作流效率的重大进步，但在广泛推广应用前仍需进行临床验证与多中心评估。