Medical Imaging AI Model Combination for Unified Analysis
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Solution Overview
Problem
Existing medical imaging technologies face challenges in effectively combining and integrating multiple artificial intelligence (AI) models for comprehensive and enhanced medical image analysis, leading to suboptimal diagnostic outcomes.
Innovation Solution
A digital platform that enables the combination and interaction of AI models, allowing for the generation of AI-enhanced layers, averaging of similar model outputs, and creation of configurable workflows, along with user interfaces for controlling and presenting unified medical imaging analysis results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple AI models are used for medical image analysis, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple AI models into a unified system architecture where models can be executed in sequence or parallel. The system integrates model management, result aggregation, and presentation layers that harmonize outputs from multiple models, thereby achieving improved diagnostic accuracy while managing system complexity through structured integration.
Solution Approach 2:
The system is designed with universal components that can handle multiple AI models with different functionalities. A single platform supports various model types, execution modes, and output formats, allowing the system to perform multiple diagnostic functions without requiring separate dedicated systems for each model.
2Adaptability or versatility
If multiple AI models are integrated, then comprehensive analysis is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces intermediary components including a model manager, result aggregator, and presentation layer that mediate between multiple AI models and the user. These intermediaries handle model selection, execution coordination, result synthesis, and visualization, shielding users from the complexity of multiple models while enabling comprehensive analysis.
Solution Approach 2:
The system segments the complex task of multi-model analysis into distinct functional layers: model management layer, execution layer, result aggregation layer, and presentation layer. Each layer handles specific aspects of the analysis process, making the overall system more manageable and easier to operate despite its comprehensive capabilities.
3Productivity
If AI model combination is implemented, then diagnostic efficiency is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring model combinations, execution parameters, and result aggregation rules. Models can be pre-selected and organized into workflows based on specific diagnostic tasks, allowing efficient execution without requiring complex real-time decision-making about model selection and integration.
Data Source
AI summary
This disclosure relates to the combining and interaction of multiple artificial intelligence (AI) models for medical image analysis. An example method includes obtaining AI models from model providers and organizing them to form associations. In response to a user request, base models are selected and provided. Additional models are further selected to combine with the base models, and medical image analysis results are presented based on applying a combination of the models to target medical image data.