Medical Device Control Parameters With Blackbox Confidence Validation
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Solution Overview
Problem
Machine-learning based medical software provides outputs that are difficult to interpret, posing risks in medical decision-making, especially when controlling medical devices that perform treatments, as the 'blackbox' nature of these systems makes it challenging to ensure the accuracy and safety of patient treatment.
Innovation Solution
A computerized system with a security module to assess health risks, a first blackbox module for machine-learned determination, a second interpretable module based on physiological knowledge, a confidence assessment module to evaluate the blackbox module's reliability, and a decision module to determine control parameters based on these inputs, ensuring safer operation of medical devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning based blackbox modules are used to determine control parameters, then the accuracy and adaptability of medical treatment is improved, but the interpretability and safety of the system deteriorates
Solution Approach 1:
The system divides the determination of control parameters into two separate modules: a blackbox module (first determination module) that provides high accuracy through machine learning, and an interpretable module (second determination module) that provides transparency through physiological models. Each module operates independently but their results are integrated through a combination function, allowing the system to benefit from both accuracy and interpretability without compromising either.
2Adaptability or versatility
If blackbox modules are used for determining control parameters, then the adaptability of treatment is improved, but the reliability and safety control deteriorates
Solution Approach 1:
The system implements a feedback mechanism where the interpretable module continuously validates the outputs of the blackbox module. The combination function uses results from both modules, allowing the interpretable module to provide corrective feedback when the blackbox module produces unreliable results. This ensures that adaptability is maintained while reliability is preserved through continuous validation.
Solution Approach 2:
The interpretable module acts as an intermediary between the blackbox module and the final control parameter determination. It translates and validates the blackbox module's outputs through physiological knowledge, ensuring that adaptive decisions from the machine learning module meet safety and reliability requirements before being applied to patient treatment.
3Device complexity
If a single blackbox module is used, then the device complexity is reduced, but the safety and reliability of medical treatment deteriorates
Solution Approach 1:
The system merges two determination modules with different characteristics (blackbox and interpretable) into a unified control system. The combination function integrates outputs from both modules to produce the final control parameters, creating a more reliable system than either module could provide alone, while maintaining manageable complexity through structured integration.
Data Source
AI summary
The computerized system comprises a processor with: a security module (5) which evaluates a potential health risk of the patient, a decision module (9) which determines a set of control parameters based on a function of the security module (5), a first blackbox module (6), a second interpretable module (7) and a confidence assessment module (8) assessing confidence in the first blackbox module (6).