Surgical ML Data Exchange for Privacy-Aware Processing
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Solution Overview
Problem
Incorporating non-traditional algorithms, such as machine learning algorithms, into medical technology presents challenges, particularly in balancing privacy and processing goals in surgical and interventional healthcare systems.
Innovation Solution
A surgical computing system is developed to manage data exchange between machine learning models and data storage, determining data exchange behaviors based on privacy implications and processing goals, and classifying data subsets to balance privacy with processing task importance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning algorithms are incorporated into medical technology to improve patient care personalization, then the quality of patient care is improved, but privacy risks increase due to handling of sensitive surgical data
Solution Approach 1:
The patent segments surgical data into multiple subsets with different privacy classifications (first, second, and third subsets with increasing privacy sensitivity). This segmentation allows the system to handle different types of data differently, enabling machine learning models to access less private data for personalization while restricting access to highly private data, thus resolving the contradiction between care personalization and privacy protection.
Solution Approach 2:
The patent introduces a surgical computing system as an intermediary between machine learning models and surgical data. This intermediary determines data exchange behaviors based on privacy implications and processing goals, acting as a mediator that enables personalized care through ML while protecting privacy by controlling what data is exchanged and under what conditions.
2Measurement precision
If highly private data is sent to machine learning models for processing, then processing accuracy is improved, but privacy protection is compromised
Solution Approach 1:
The patent applies local quality by assigning different privacy classifications to different subsets of surgical data. Each data subset receives different levels of protection and access control based on its specific privacy sensitivity. This allows machine learning models to access sufficient data for accurate processing while ensuring that highly private data is protected through restricted access and selective data exchange.
3Object-affected harmful factors
If data exchange is restricted to protect privacy, then privacy protection is improved, but processing task effectiveness deteriorates
Solution Approach 1:
The patent implements dynamic data exchange behavior determination based on processing goals and privacy implications. The system can adaptively adjust what data is exchanged and to which machine learning models based on the specific processing task requirements. This dynamic approach ensures that privacy protection is maintained while processing effectiveness is optimized for each specific task.
4Adaptability or versatility
If multiple machine learning models are used to process different data subsets, then processing capability is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal surgical computing system that can handle multiple machine learning models and multiple data subsets through a unified framework. The system determines data exchange behaviors based on processing goals and privacy implications, providing a multi-functional platform that manages diverse ML models and data types without requiring separate systems for each, thus improving processing capability while controlling system complexity.
Data Source
AI summary
Systems, methods, and instrumentalities are disclosed for a surgical computing system with support for machine learning model interaction. Data exchange behavior between machine learning (ML) models and data storages may be determined and implemented. For example, data exchange may be determined based on privacy implications associated with a ML model and/or data storage. Data exchange may be determined based on processing goals associated with ML models.