AI Error Detection in Medical Image Processing
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Solution Overview
Problem
There is a need for a system and method to facilitate error detection and correction in medical image records processed by artificial intelligence engines, particularly in DICOM objects, to prevent erroneous results from being transmitted to Picture Archiving and Communication Systems (PACS).
Innovation Solution
A method involving an artificial intelligence engine that processes medical images, detects errors using a server emulator, produces a corrected test result, and transmits it to a PACS server, including an error log that identifies the application programming interface responsible for the error, ensuring the corrected result does not include the original error and is properly formatted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error detection and correction mechanisms are implemented in the AI engine processing pipeline, then the reliability of medical image processing results is improved, but the device complexity increases
Solution Approach 1:
The patent implements error detection and correction mechanisms as preliminary actions within the AI engine processing pipeline. The server emulator detects potential errors in test results before they are transmitted to the PACS system, and correction mechanisms are applied in advance to prevent erroneous data propagation. This approach improves reliability by addressing issues proactively rather than reactively.
Solution Approach 2:
The patent introduces a server emulator as an intermediary component between the AI engine and the PACS system. This emulator acts as a mediator that intercepts test results, performs error detection and validation, and either allows transmission of corrected results or blocks transmission of erroneous results. This intermediary layer improves overall system reliability without requiring complex modifications to the core AI engine or PACS system.
2Manufacturing precision
If error detection and correction steps are added to the processing workflow, then the manufacturing precision of test results is improved, but the loss of time in processing increases
Solution Approach 1:
Error detection and correction operations are performed as preliminary actions during the test result generation process, before final transmission to the PACS system. By integrating these checks into the existing processing workflow rather than adding post-processing steps, the system achieves high precision in test results while minimizing additional time overhead.
Solution Approach 2:
The server emulator is designed to perform error detection and validation operations efficiently, skipping unnecessary validation steps when results are already correct and only performing detailed checks when anomalies are detected. This approach maintains high precision in test results while reducing the average time overhead by avoiding redundant validation operations.
Data Source
AI summary
A system, method, and apparatus for detecting errors in an artificial intelligence engine. The method includes processing a medical image of a patient at an artificial intelligence engine, and producing a first test result at the artificial intelligence engine based on the medical image. The method also includes detecting an error in the first test result using a server emulator, and producing a second test result that corrects the error in the first test result. In addition, the method includes transmitting the second test result from the artificial intelligence engine to a picture archiving and communication systems server.