Intelligent Healthcare Imaging Platform: A VLM-Based Framework for Automated Medical Image Analysis and Clinical Report Generation

2025-09-16 · Source: cs.AI updates on arXiv.org · Field: Health & Wellbeing — Medical Devices & Health Technology, Clinical Care & Medical Practice, Health & Medical Research · Depth: Expert, quick

Summary

A new intelligent multimodal framework for medical image analysis, leveraging Vision-Language Models (VLMs), has been developed to automate tumor detection and clinical report generation. This system integrates Google Gemini 2.5 Flash and supports various imaging modalities including CT, MRI, X-ray, and Ultrasound. It combines visual feature extraction with natural language processing for contextual image interpretation, incorporating coordinate verification and probabilistic Gaussian modeling for anomaly distribution. The platform generates detailed medical illustrations, overlay comparisons, and statistical representations to enhance clinical confidence, achieving an 80-pixel average deviation for location measurement. It utilizes precise prompt engineering and textual analysis to extract structured clinical information, features a user-friendly Gradio interface, and demonstrates zero-shot learning capabilities to reduce reliance on large datasets.

Key takeaway

For radiologists and diagnostic imaging specialists evaluating new AI tools, this VLM-based framework offers automated anomaly detection and report generation across diverse modalities. Your team should consider its zero-shot learning and multimodal capabilities for enhancing workflow efficiency and diagnostic support. However, prioritize clinical validation and multi-center evaluation before widespread adoption to ensure reliability.

Key insights

A VLM-based framework automates medical image analysis and clinical report generation across multiple imaging modalities.

Principles

• Integrate VLMs for multimodal medical diagnostics.
• Employ zero-shot learning to reduce data dependency.

Method

The framework extracts visual features, processes natural language, verifies coordinates, and uses Gaussian modeling for anomaly distribution, generating multi-layered visualizations and structured clinical reports via prompt engineering.

In practice

• Use Google Gemini 2.5 Flash for medical imaging tasks.
• Implement Gradio for user-friendly clinical interfaces.

Topics

• Intelligent Healthcare Imaging
• Vision-Language Models
• Google Gemini 2.5 Flash
• Automated Tumor Detection
• Clinical Report Generation

Best for: NLP Engineer, Computer Vision Engineer, AI Scientist, Research Scientist, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.