Feature-Embedding Architecture for Data-Free Medical AI Deployment
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Solution Overview
Problem
Deploying large-scale machine learning models in medical environments to analyze procedure data is challenging due to the need to transfer sensitive data out of protected data environments, which is costly and inefficient, and labeling data is a time-consuming process.
Innovation Solution
A segregated data processing architecture is used where sensitive medical procedure data is processed locally, and feature embeddings are generated without transmitting data, allowing the use of pre-trained AI models tailored to the environment through similarity AI models and video data retrieval algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensitive medical procedure data is transmitted for AI model training, then model performance is improved, but data security and transmission costs deteriorate
Solution Approach 1:
The patent introduces feature embeddings as an intermediary representation that captures essential characteristics of medical procedures without containing identifiable patient information. These embeddings serve as a mediator between raw sensitive data and AI model training, enabling performance improvement while maintaining data security within protected environments like FHIR servers.
Solution Approach 2:
The patent extracts only the necessary features from sensitive medical procedure data to create feature embeddings, separating essential training information from identifying and sensitive details. This extraction process allows model training to proceed with purified data that maintains performance while eliminating security risks associated with transmitting complete raw datasets.
2Measurement precision
If AI models are trained on labeled medical procedure data, then analysis accuracy is improved, but data labeling time and costs increase
Solution Approach 1:
The patent performs preliminary extraction of feature embeddings from raw medical procedure data before the main AI model training process. This preliminary action prepares structured, labeled-like representations that capture essential procedural information, reducing the need for time-consuming manual labeling while maintaining analysis accuracy in the subsequent training phase.
3Adaptability or versatility
If large-scale AI models are deployed in protected data environments, then local analysis capability is improved, but data transmission requirements worsen
Solution Approach 1:
The patent extracts feature embeddings locally within protected environments such as FHIR servers, obtaining compact representations of medical procedure data without transmitting the complete raw datasets. This extraction enables large-scale AI models to be deployed locally with reduced data transmission requirements, as only essential feature information needs to be processed rather than entire patient records.
Data Source
AI summary
The arrangements disclosed herein relate to systems, apparatuses, methods, and non-transitory processor-readable media for receiving, from a protected data environment, at least one feature embedding generated from data of a medical procedure, determining, using a similarity machine-learning model, a set of historical data of a plurality of medical procedures similar to the received feature embedding, identifying one or more analysis machine learning-models updated using the set of historical data, and providing, based on the one or more identified machine-learning models, an analysis machine-learning model for the protected data environment.