Telemedicine Remote Sensing Diagnostics with Secure ML Approximation
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Solution Overview
Problem
Network connections in telemedicine systems are vulnerable to attacks, compromising the secure communication of sensitive patient information, especially in urgent situations where immediate attention is needed.
Innovation Solution
A system and method for telemedicine diagnostics through remote sensing, which involves a computing device that receives physiological data from sensors, calculates changes in data sets, and generates clinical measurement approximations using machine-learning processes and training data to provide secure and accurate remote diagnostics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If network connections are used for telemedicine communication, then remote diagnostics capability is improved, but security vulnerability to attacks increases
Solution Approach 1:
The patent introduces a secure communication layer as an intermediary between the telemedicine system and external networks. This mediator implements encryption protocols and authentication mechanisms that protect patient data while allowing remote diagnostics to function, thus resolving the contradiction between accessibility and security.
2Measurement precision
If machine-learning processes are used to generate clinical measurements from sensor data, then accuracy of remote measurements is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine-learning models offline with large datasets before deployment. The trained models are then deployed to the telemedicine device, where they perform rapid inference on sensor data. This approach achieves high measurement precision while reducing real-time computational complexity, as the heavy training work was performed in advance.
Data Source
AI summary
A system for telemedicine diagnostics through remote sensing includes a computing device configured to initiate a communication interface between the computing device and a client device operated by a human subject, wherein the secure communication interface includes an audiovisual streaming protocol, receive, from at least a remote sensor at the human subject, a plurality of current physiological data, generate a clinical measurement approximation as a function of the change of a first discrete and a second discrete set of current physiological data, wherein generating further comprises receiving approximation training data correlating physiological data with clinical measurement data, training a measurement approximation model as a function of the training data and a machine-learning process, and generating the clinical measurement approximation as a function of the current physiological data and the measurement approximation model, and presenting the clinical measurement approximation to a user of the computing device using the secure communication interface.