AI Medical Control Using Multi-Modal Data Suitability Feedback
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Solution Overview
Problem
Existing medical systems face challenges in handling multi-modal data of various dimensions, leading to inconsistencies in data suitability and reliability, which affects the accuracy and precision of medical operations.
Innovation Solution
A medical system control method utilizing a first artificial intelligence model to generate condition change data based on multi-modal data, adjusting control conditions when suitability is below a reference value, and performing operations when suitability is met or above, with a second AI model assessing reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple types of data are used in medical systems, then the comprehensiveness of information is improved, but the reliability of medical operations deteriorates due to differences in suitability between individual data types
Solution Approach 1:
The patent introduces a suitability evaluation mechanism that dynamically adjusts control conditions based on the quality and suitability of multi-modal data. By monitoring data suitability parameters and modifying operational parameters accordingly, the system maintains high reliability while utilizing diverse data types for comprehensive medical operations.
2Measurement precision
If specialized AI models are used for specific data types, then the accuracy for that specific data is improved, but the versatility of the system deteriorates when handling mixed multi-modal data
Solution Approach 1:
The patent implements a unified AI model architecture that can process multiple types of medical data (images, time series, sensor data) through a common framework. This universal model maintains high accuracy across different data modalities while enabling the system to handle mixed multi-modal data effectively, thus achieving both precision and versatility.
Solution Approach 2:
The patent introduces a data suitability evaluation mechanism as an intermediary layer between data acquisition and AI processing. This intermediary assesses the quality and suitability of each data type before feeding it to the AI model, enabling the system to maintain accuracy by filtering or adjusting data based on its suitability while preserving versatility in handling diverse data types.
3Reliability
If additional suitability determination processes are added, then the reliability of data usage is improved, but the device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the suitability evaluation continuously monitors data quality and provides feedback to adjust control conditions. This feedback loop enables the system to maintain high reliability by adapting to data quality variations while keeping the control process manageable through automated, rule-based adjustments rather than complex manual intervention.
Data Source
AI summary
A medical system control method that is performed by a computing device including at least one processor according to one embodiment of the present invention includes: obtaining multi-modal data from a medical system that operates according to set control conditions; generating condition change data for changing the control conditions of the medical system based on the multi-modal data by using a first artificial intelligence model; and controlling the medical system based on the condition change data.