Medical Imaging LLM Function Orchestration for Crowded GUI Workflows
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Solution Overview
Problem
Medical imaging systems for structural heart interventions face usability issues due to a crowded graphical user interface (GUI) with numerous features, leading to increased search time and operational stress, which can delay critical procedures.
Innovation Solution
A large language model (LLM) AI generates an executable program to automate the selection of medical imaging functions based on user input, allowing for intuitive interaction and risk management through confidence monitoring and user iteration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If more advanced features are added to the medical imaging system, then the system's functionality and versatility are improved, but the GUI becomes crowded and more complex, reducing usability and increasing search time
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the user and the complex medical imaging system. The AI assistant processes natural language queries and automatically selects and executes appropriate imaging functions, shielding users from the underlying complexity of the GUI while maintaining access to advanced features. This mediator approach allows users to interact with the system using simple language rather than navigating complex menus.
Solution Approach 2:
The system implements self-service through automated function selection and execution based on user intent. The AI assistant autonomously determines which imaging functions to invoke and in what sequence, eliminating the need for users to manually navigate through crowded GUI panels. The system serves itself by automatically translating user goals into actionable technical operations.
2Adaptability or versatility
If more features are provided in the GUI, then the system's capability is improved, but the user experiences increased operational stress and distraction, negatively impacting operation efficiency
Solution Approach 1:
The AI assistant acts as a buffer that absorbs the complexity of multiple features, allowing users to interact with the system through simple natural language rather than managing numerous GUI elements. This intermediary reduces cognitive load and operational stress while maintaining access to the full system capability.
Solution Approach 2:
The patent replaces the mechanical interaction with crowded GUI buttons and panels with a cognitive system that understands natural language. Instead of manually selecting features through complex menus, users can describe their needs in plain language, and the AI assistant translates this into appropriate technical operations, significantly reducing operational stress.
3Adaptability or versatility
If a crowded GUI with numerous features is used, then the system's functionality is improved, but the search time and delay in critical procedures increase
Solution Approach 1:
The system automatically determines and executes the appropriate imaging functions based on user intent without requiring manual search or navigation. The AI assistant self-services by autonomously selecting and sequencing the necessary features, eliminating search time entirely while maintaining access to all system functionalities.
Solution Approach 2:
The system performs preliminary action by pre-processing user intent and automatically determining the optimal sequence of imaging functions before execution. This preliminary processing eliminates the need for users to search through features during critical procedures, as the system has already identified and prepared the necessary operations.
Data Source
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AI summary
For decision making in medical image processing, a large language model (LLM) artificial intelligence (AI) (602) generates a program (604) calling a series of available features to answer a user request. Rather than navigating through various functions (606) in the GUI, the user may input a question, and the LLM AI (602) then programs the medical imaging system to implement the functions (606) to answer the question.