Dialysis System AI Constraint Validation
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Solution Overview
Problem
Dialysis systems face challenges in adapting operational parameters using artificial intelligence without deviating from approved design parameters, risking patient safety and regulatory compliance due to lack of integrity in state-related data and untraceable changes.
Innovation Solution
Implementing a mechanism that constrains adaptive optimizations using SHA256 hash algorithms and a supervisory module to ensure that AI-driven changes do not exceed predefined constraints, reverting to previous valid states if parameters exceed limits, thus maintaining safety and compliance with FDA regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If adaptive optimizations using artificial intelligence are implemented in dialysis systems, then operational efficiency and treatment personalization are improved, but system reliability and regulatory compliance deteriorate due to untraceable parameter changes and potential deviation from approved design parameters
Solution Approach 1:
The system implements a feedback mechanism where the supervisor module continuously monitors operational parameters modified by AI and compares them against pre-defined constraints. When parameters deviate from approved ranges, the system automatically detects and corrects the deviation, ensuring regulatory compliance while allowing AI-driven adaptability within safe boundaries.
Solution Approach 2:
A supervisor module is introduced as an intermediary between the AI system and the dialysis operation modules. This mediator validates AI-generated parameter changes before implementation, ensuring that adaptive optimizations do not compromise system reliability or violate regulatory requirements.
2Productivity
If adaptive optimizations using artificial intelligence are implemented in dialysis systems, then treatment effectiveness is improved, but data integrity deteriorates due to lack of traceability in state changes
Solution Approach 1:
The system performs preliminary validation by pre-defining constraint boundaries for all operational parameters before AI optimization begins. This preliminary action ensures that any AI-driven changes remain within traceable and approved ranges, maintaining data integrity while enabling treatment effectiveness improvements.
Solution Approach 2:
The supervisor module provides continuous feedback on parameter changes, tracking and recording all state transitions. This feedback mechanism ensures complete traceability of AI-driven modifications, preventing information loss while maintaining treatment effectiveness.
3Adaptability or versatility
If operational parameters are modified based on AI feedback, then system adaptability is improved, but device complexity increases due to additional constraint validation mechanisms
Solution Approach 1:
The system architecture is segmented into distinct functional modules: AI optimization modules for adaptability and a supervisor module for constraint validation. This segmentation allows each module to perform its specialized function independently, managing complexity while maintaining operational adaptability.
Solution Approach 2:
The supervisor module serves as an intermediary layer that manages the complexity of constraint validation without interfering with the AI's adaptive capabilities. This mediator handles the computational overhead of validation, keeping the overall system manageable while enabling operational adaptability.
Data Source
AI summary
Constraining adaptive optimizations of a state of an operation module of a medical device includes determining if a new state has at least one operational parameter that is outside a constraint that has been provided to the medical device in a non-repudiable manner, accepting the new state if no operational parameters are outside any of the constraints, and reverting the medical device to a previous valid state if at least one operational parameter is outside at least one of the constraints. The adaptive optimizations may be provided using artificial intelligence along with relevant inputs thereto. The medical device may be a dialysis system. Constraint data may be provided to the medical device along with a one-way hash value of the constraint data using, for example, a SHA 256 hash. The one-way hash value may be digitally signed using a private key that is part of a public/private key pair.