AI-Based Standard-of-Care Support with Edge-Cloud Diagnostics
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Solution Overview
Problem
Existing medical diagnostic devices face challenges with portability, accuracy, reliability, and computational limitations, particularly in integrating AI capabilities and ensuring data privacy, while also lacking real-time personalized insights and secure integration in virtual environments.
Innovation Solution
A hand-held device integrating multiple health sensors with optimized power management, AI processing across on-device, edge, and cloud layers, and secure communication protocols, along with advanced biometric identification using eye vasculature patterns and blood flow characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI algorithms are integrated into portable medical devices, then diagnostic accuracy and personalized insights are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments AI processing across multiple layers: simple algorithms run on the portable device, intermediate processing occurs at edge computing nodes, and complex model training is performed in the cloud. This segmentation allows the device to provide accurate diagnostics without requiring the full computational power of a single powerful computer.
Solution Approach 2:
The patent transitions from on-device processing to a distributed computing architecture that adds temporal and spatial dimensions to processing. Instead of all processing occurring at one location, it is distributed across device, edge, and cloud layers, enabling sophisticated AI without overwhelming the portable device.
2Adaptability or versatility
If multiple sensors are integrated into a single portable device, then functionality and comprehensive health monitoring are improved, but power consumption increases
Solution Approach 1:
The system employs periodic sampling and event-driven data transmission rather than continuous operation. Sensors sample data at appropriate intervals, and the system transmits data to the cloud only when necessary, reducing overall power consumption while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
An intermediary power management system coordinates sensor operation and data transmission to optimize power consumption. The system intelligently manages the power budget across multiple sensors, enabling comprehensive functionality without excessive power drain.
3Device complexity
If biometric identification uses only pattern recognition, then device simplicity is maintained, but security is compromised due to potential spoofing
Solution Approach 1:
The system merges multiple identification modalities: pattern recognition from eye images and physiological verification through blood flow measurement. This combination makes spoofing extremely difficult while maintaining relative simplicity, as the system only needs to capture images and measure blood flow rather than implement complex multi-sensor systems.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate, real-time health monitoring and personalized insights, enhances diagnostic efficiency, and ensures secure data handling, while providing seamless integration of healthcare services in virtual environments.
Implementation Method 1
a measure of a dynamically changing blood flow characteristic
Data Source
AI summary
An AI-based system and method for supporting differential diagnosis and standard of care in healthcare. The method involves receiving patient information from various sources, including patient-reported symptoms, physician notes, and sensor data from medical devices. The patient information is preprocessed and analyzed using deep learning models to generate a ranked list of potential diagnoses, each associated with likelihood scores and key contributing factors. The potential diagnoses are provided to physicians via an interactive interface, and physician feedback is collected to fine-tune the AI models using reinforcement learning. The method aims to enhance physician decision-making, improve diagnostic efficiency, and ensure adherence to the standard of care by leveraging AI's ability to analyze vast amounts of data more effectively than human physicians.