Distributed AI Model Orchestration for Medical Imaging Analysis
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Solution Overview
Problem
Current medical imaging data processing systems face challenges in efficiently analyzing and managing large volumes of data from various medical procedures, including limited computing resources and the need for timely and accurate analysis of high-resolution images, which can lead to inconsistencies and delays in diagnosis.
Innovation Solution
The implementation of an AI-enhanced workflow that utilizes distributed AI models and an inference engine for orchestration, allowing for the selection and execution of appropriate AI models at different locations, such as on-premise or cloud servers, to process medical imaging data efficiently and accurately, while ensuring model validation and governance to maintain data integrity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed AI models are used to process medical imaging data, then analysis accuracy and diagnostic efficiency are improved, but system complexity and computational resource requirements increase
Solution Approach 1:
The patent segments the AI processing system into multiple specialized models distributed across different locations (on-premise and cloud). Each model handles specific imaging tasks or data types, allowing the system to achieve high diagnostic accuracy through specialized processing while managing complexity through modular organization and distributed deployment.
2Speed
If high-resolution medical imaging data is analyzed in real-time, then diagnostic speed is improved, but computational resource consumption increases
Solution Approach 1:
The patent implements local quality by deploying appropriate AI models at appropriate locations based on data characteristics and processing requirements. Simple or routine imaging analyses are handled by lightweight on-premise models for rapid local processing, while complex or specialized analyses are routed to cloud-based models with greater computational resources, optimizing the balance between speed and resource consumption.
3Productivity
If multiple AI models are deployed across different locations, then processing capability and diagnostic efficiency are improved, but data integrity management and model governance become more difficult
Solution Approach 1:
The patent implements comprehensive feedback mechanisms including model validation pipelines that continuously monitor and verify model outputs, performance tracking systems that compare results across different locations, and automated governance protocols that enforce data integrity standards. This feedback loop ensures that distributed models maintain consistent, reliable outputs while leveraging the enhanced processing capability of multiple locations.
Data Source
AI summary
Systems and methods for processing electronic imaging data obtained from medical imaging procedures, with use of trained artificial intelligence (AI) models, are disclosed herein. In an example, a use of a medical evaluation workflow involving an AI model includes: obtaining image data and non-image data associated with a medical imaging study; using at least one AI model to analyze the image data, with the trained AI model being validated with a defined governance standard to identify a characteristic or particular type of characteristic; identifying the characteristic with the AI model; and communicating the identified characteristic to a location associated with evaluation of the medical imaging study.