AI Application Selection via Automated Performance Scoring
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Solution Overview
Problem
AI orchestrator systems face challenges in selecting appropriate AI applications for digital image processing due to limited or biased data in individual algorithms, narrow generalization capabilities, and lack of real-time advising tools, leading to uncertainty in performance and vendor reliability.
Innovation Solution
A learning-based method that automatically receives user requests for exams, identifies exam type clusters, detects applicable AI applications, runs them on relevant test sets to generate scores, and recommends the highest-scoring application, leveraging feedback and test data for improved selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple AI applications from different vendors are available for selection, then the system has greater versatility and choice, but the difficulty of selecting the appropriate application increases due to limited or biased data in individual algorithms
Solution Approach 1:
The system implements a feedback mechanism by automatically running each applicable AI application on relevant test sets and generating performance scores. These scores provide objective feedback that guides the selection process, transforming the difficult subjective selection into a data-driven decision based on measured performance across multiple vendors' applications
Solution Approach 2:
The patent replaces manual expert selection (mechanical process) with an automated machine learning-based scoring system. The system automatically clusters exam types, detects applicable AI applications, runs them on test sets, and generates scores without human intervention, substituting the manual selection mechanism with an automated computational one
2Ease of operation
If AI applications are selected based on vendor recommendations or limited data, then the selection process is simpler, but the reliability and accuracy of the selected application decreases
Solution Approach 1:
The system performs preliminary actions by automatically running each AI application on relevant test sets before final selection. This advance testing and scoring ensures that the selected application has been pre-validated on actual test data, increasing reliability while keeping the final selection process simple for the user
Solution Approach 2:
The system enables self-service by automatically performing the entire selection process including clustering exam types, detecting applicable applications, running test sets, and generating scores without requiring human expertise. The system serves itself by making data-driven selections, improving both reliability and operational simplicity
3Use of energy by moving object
If manual selection of AI applications is performed, then the system requires less computational resources, but the productivity and speed of providing recommendations decreases
Solution Approach 1:
The system segments the selection process into distinct automated components: exam type clustering, AI application detection, test set execution, and score generation. This segmentation allows efficient parallel processing and automated workflow management, improving productivity while managing computational resources through structured processing
4Measurement precision
If AI applications are tested on extensive test sets to ensure accuracy, then the measurement precision improves, but the time and computational resources required increase
Solution Approach 1:
The system applies partial action by selecting and running only the most relevant test sets for each AI application based on the exam type cluster and application capabilities. This avoids exhaustive testing of all possible test sets while still achieving sufficient measurement precision for reliable selection
Solution Approach 2:
The system changes parameters by dynamically selecting test sets based on the specific exam type and AI application being evaluated. Rather than using fixed extensive test sets for all applications, the system adapts the testing parameters to match the specific context, improving precision while reducing unnecessary testing time
Data Source
AI summary
An embodiment for learning-based automatic selection of artificial intelligence applications. The embodiment may receive a user request for an exam, the user request including exam information. The embodiment may automatically identify an exam type cluster corresponding to the received exam information. The embodiment may automatically detect applicable AI applications corresponding to the identified exam type cluster. The embodiment may automatically run each applicable AI application on a series of relevant test sets to generate a score for each applicable AI application. The embodiment may automatically recommend to a user a highest-scoring applicable AI application.