Multimodal GenAI Medical Imaging Report Generation Framework: Practice of Integrating Edge Optimization and Explainable AI

This project is a multimodal AI system for medical scenarios. It combines visual encoders and large language models to generate automated radiology reports. Key features include support for edge deployment, multilingual output, and explainable AI, aiming to address medical pain points such as radiologist shortages and limitations of traditional AI tools.

Medical imaging diagnosis is a core part of modern medicine, but the global shortage of radiologists is severe. In many regions, physicians' workload far exceeds reasonable limits, leading to delayed diagnosis and increased risk of missed diagnoses. Traditional AI-assisted tools can only output simple classification labels and cannot generate detailed reports that meet clinical standards. Most rely on cloud computing, making deployment difficult in scenarios with data privacy concerns or limited network access. This project addresses these pain points by building an edge-optimized multimodal generative AI framework that automatically generates structured radiology reports and provides explainable AI evidence to support clinical decision-making.

Multimodal Architecture Design

• Visual Encoder: Uses CNN/Vision Transformer pre-trained on medical images, combined with multi-scale feature fusion and lesion area attention mechanisms to extract high-dimensional visual features.
• Medical Language Model: Trained on large-scale medical text and adapted to radiology report corpora to enable structured report generation and accurate output of professional terminology.

Edge Optimization Strategies

• Model Compression: Reduces model size and computational load through knowledge distillation, INT8/INT4 quantization, and pruning optimization.
• Inference Acceleration: Improves runtime efficiency on edge devices using operator fusion, dynamic batching, and caching mechanisms.

Explainable AI Integration

• Attention Visualization: Provides spatial, cross-modal, and temporal attention maps to show the model's focus areas and the correspondence between visual and text information.
• Heatmap Generation: Supports techniques like Grad-CAM and Integrated Gradients, with uncertainty estimation to mark the model's confidence interval.

Structured Report Generation

Automatically outputs standardized reports including examination information (patient information, examination type, etc.), imaging findings, impression diagnosis, and recommended measures.

Multilingual Support

• Offline translation to generate multilingual reports without internet connection;
• Ensures consistency of medical terminology across different languages;
• Adapts to report format habits in different regions.

Clinical Validation Support

• Confidence prompt: Proactively prompts physicians to review when the model is uncertain;
• Comparative reference: Links historical images and reports to assist longitudinal analysis;
• Edit tracking: Records physician modifications for continuous model improvement.

Primary Care Empowerment

• Provides preliminary diagnosis references to shorten patient waiting time;
• Serves as a training tool to improve primary physicians' image reading ability;
• Supports teleconsultation to connect with experts from higher-level hospitals.

Emergency Rapid Screening

• Automatically alerts for acute conditions such as cerebral hemorrhage and pulmonary embolism;
• Priority sorting ensures critical patients are handled first;
• Provides uninterrupted preliminary screening services during non-working hours.

Research and Quality Control

• Structured annotation of large-scale imaging data;
• Automatic assessment of diagnostic consistency;
• Quantitative analysis of radiologists' workload.

The project design fully considers medical AI ethical requirements:

• Data Security: Local processing avoids external transmission of patient data;
• Transparency: Explainable AI allows physicians to understand the basis for judgments;
• Responsibility Definition: Clearly positions AI as an assistant, with final diagnostic authority remaining with physicians;
• Fairness: Evaluates performance across different populations, devices, and hospital levels.

Future Development Directions

1. Multimodal fusion integrating multi-source data such as imaging, laboratory tests, and medical records;
2. Temporal modeling to support follow-up imaging comparison analysis;
3. Personalized adaptation to adjust report style according to physician preferences;
4. Multi-center federated learning under privacy protection.

Summary

This project demonstrates the great potential of multimodal GenAI in the medical field. Edge optimization enables the deployment of advanced AI capabilities in resource-constrained environments, while explainable AI enhances model transparency and trust. Multilingual support promotes medical equity. As technology matures, such systems are expected to become powerful assistants for radiologists, ultimately benefiting more patients.