Medical Scanner Fleet Control Using LLM Metadata Analysis
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Solution Overview
Problem
Existing systems for managing and analyzing fleets of medical imaging devices, such as MRI scanners, face inefficiencies in dashboard navigation, inability to interpret new log and DICOM tag data, and require constant algorithm updates, leading to suboptimal operation and maintenance.
Innovation Solution
A method utilizing a large language model (LLM) to analyze measurement metadata from various databases, determine program code for controlling or regulating imaging devices, and provide dynamic, user-specific adjustments to energy consumption and acquisition protocols, with optional user input and validation checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional dashboard systems with fixed visualizations are used, then current scanner fleet status can be monitored, but the system cannot adapt to new log information and DICOM tags without constant algorithm updates
Solution Approach 1:
The system employs large language models that autonomously interpret and analyze measurement metadata without requiring manual algorithm updates. The LLMs self-adapt to new log information and DICOM tags by processing them through natural language understanding, eliminating the need for constant human intervention to update analysis algorithms.
Solution Approach 2:
The patent replaces traditional mechanical algorithmic processing with cognitive-based large language models. Instead of fixed computational algorithms that require updating, the system uses LLMs that naturally understand and adapt to new data formats through their language processing capabilities, substituting rigid mechanical processing with flexible cognitive processing.
2Loss of information
If multiple fixed dashboards are provided for different analysis questions, then comprehensive scanner fleet information can be visualized, but users face difficulty finding the right dashboard and must navigate multiple dashboards for follow-up questions
Solution Approach 1:
The system provides a universal natural language interface that can answer any fleet analysis question without requiring users to navigate multiple specialized dashboards. The LLM-based system handles diverse analysis tasks (energy consumption, protocol efficiency, idle times, etc.) through a single unified interface, making the system multi-functional and eliminating navigation complexity.
Solution Approach 2:
The large language model acts as an intermediary between the user and the complex scanner fleet data. Instead of users directly navigating multiple dashboards, the LLM mediates by understanding natural language queries and translating them into appropriate data analyses, providing a simplified interface that masks the underlying data complexity.
3Reliability
If traditional fixed editing means and visualizations are used, then standard fleet monitoring can be performed, but the system does not reflect the way of thinking of the person conducting the analysis and is not appropriate for specific fleet problems
Solution Approach 1:
The system transitions from static fixed visualizations to dynamic natural language-based analysis. The LLM adapts its responses based on the specific questions and context provided by users, allowing the interface to dynamically adjust to different user thinking patterns and specific fleet problems rather than forcing users to adapt to fixed visualization templates.
Data Source
AI summary
A computer-implemented method for controlling or regulating an imaging medical device in a fleet of imaging medical devices of a predetermined modality. The computer-implemented method includes accessing at least one digital database in order to receive measurement metadata with respect to at least one image acquisition or with respect to the imaging medical device. The image acquisition was performed using the imaging medical device. The received measurement metadata is analyzed using a large language model (LLM) with respect to at least one technical parameter of the image acquisition and/or of the imaging medical device. A program code is determined based on the analysis of the at least one technical parameter. The program code is determined for controlling or regulating the imaging medical device. The determined program code is provided.