AI Pre-Processor for Radiology Reading Error Reduction
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Solution Overview
Problem
The interpretation of radiological studies is prone to diagnostic errors, with up to 17% of hospital adverse events attributed to radiology practice, largely due to radiologist fatigue and inadequate verification of interpretations, leading to misdiagnoses such as lymphoma misinterpreted as hematoma or confusion between lung consolidation causes.
Innovation Solution
A pre-processor component for a machine learning system that processes medical data by encoding initial findings and images, combining contextual data, and providing combined encoded data to a machine learning model to suggest alternative or confirmatory findings, reducing reading errors by analyzing radiologist interpretations in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radiologist interpretation is used alone, then diagnostic speed is maintained, but diagnostic accuracy deteriorates due to fatigue and human error
Solution Approach 1:
The patent introduces an AI system as an intermediary between the radiologist and the final diagnosis. The AI processes medical images and provides alternative finding suggestions, acting as a mediator that enhances radiologist decision-making without replacing human interpretation. This resolves the contradiction by maintaining system simplicity from the user perspective while incorporating sophisticated diagnostic support.
Solution Approach 2:
The system implements feedback by analyzing the radiologist's initial findings and providing alternative suggestions based on AI analysis. This closed-loop feedback mechanism allows continuous verification and correction of diagnostic interpretations, improving reliability while keeping the workflow integrated and manageable for radiologists.
2Reliability
If AI alternative finding suggestion is implemented, then reading error reduction is achieved, but processing time increases
Solution Approach 1:
The AI system performs partial analysis by focusing on generating alternative finding suggestions rather than complete diagnostic interpretation. This partial action approach provides sufficient diagnostic support to reduce errors while avoiding the time overhead of full AI-based re-interpretation, thus balancing accuracy improvement with time efficiency.
Solution Approach 2:
The system performs preliminary AI analysis of medical images before the radiologist finalizes their interpretation. By providing alternative finding suggestions in advance, the system enables proactive error prevention without requiring time-consuming post-analysis verification, thus reducing reading errors while maintaining efficient workflow.
3Measurement precision
If comprehensive contextual data is processed, then diagnostic precision is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the most relevant contextual data elements needed for generating alternative finding suggestions, rather than processing all available patient information. This selective extraction approach maintains high diagnostic precision by focusing on critical factors while reducing computational complexity by excluding unnecessary data processing.
Solution Approach 2:
The computational task is segmented into distinct modules: image encoding, finding encoding, contextual data encoding, and alternative suggestion generation. This segmentation allows each component to process specific data types efficiently, improving diagnostic precision through specialized processing while managing overall computational complexity through modular architecture.
Data Source
AI summary
A pre-processor (PP) component and related method for a machine learning system (MLS) for processing medical data. The preprocessor comprises an input interface (IN) for receiving a human generated initial finding for a patient and a medical image to which the said finding pertains. An encoder (ENC) DPS of preprocessor encodes the finding and the medical image into encoded data, including encoded image data and encoded finding data. A combiner (COM) component of preprocessor combines the encoded finding and the encoded image data into combined encoded data. An output interface (OUT) provides the combined encoded data to the machine learning system. More robust machine learning performance may be achieved with the proposed pre-processor (PP).