AI Diagnostic System with Explainable Feedback Loop
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Solution Overview
Problem
The shortage of pathologists leads to increased workloads and delays in medical diagnosis, necessitating the development of an efficient image diagnostic system using artificial intelligence to support pathological image analysis.
Innovation Solution
An image diagnostic system that includes a control unit, diagnosis unit, report output unit, selection unit, extraction unit, and correction unit, utilizing machine learning models to estimate diagnosis results, extract determination basis information, and correct findings for improved diagnostic accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pathological diagnosis is performed manually by pathologists, then diagnostic accuracy can be maintained, but the workload increases and diagnosis period extends due to pathologist shortage
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the pathological image and the pathologist. The AI performs preliminary analysis, extracts features, and generates draft diagnosis reports, allowing pathologists to focus on final verification and complex cases. This mediator approach maintains diagnostic accuracy while significantly increasing throughput by handling routine analysis tasks automatically.
Solution Approach 2:
The diagnostic process is segmented into multiple stages: AI-based preliminary analysis, feature extraction, draft report generation, and pathologist verification. This segmentation allows routine tasks to be automated while preserving human expertise for critical decision-making, thereby maintaining accuracy while improving overall productivity.
2Productivity
If AI is used for pathological diagnosis, then productivity increases, but the interpretability and trustworthiness of diagnosis results decrease
Solution Approach 1:
The system provides feedback to pathologists by presenting the AI's analysis results, confidence scores, and extracted features in an interpretable format. Pathologists can review the AI's reasoning process, verify findings, and provide corrections, creating a feedback loop that maintains trustworthiness while leveraging AI productivity benefits.
Solution Approach 2:
The AI system acts as an interpretable intermediary by providing detailed explanations of its analysis, including extracted features, confidence levels, and reasoning processes. This transparency allows pathologists to understand and trust AI-generated diagnoses while maintaining high productivity.
3Reliability
If comprehensive determination basis information is extracted, then diagnostic reliability improves, but system complexity and processing time increase
Solution Approach 1:
The system extracts only the most relevant determination basis information needed for diagnosis, rather than processing all possible data. By selectively extracting key features and findings, the system maintains high diagnostic reliability while avoiding unnecessary complexity and processing overhead.
Solution Approach 2:
The system applies different levels of analysis depth to different regions or aspects of the pathological image based on their diagnostic importance. Critical areas receive more detailed analysis while less important areas are processed more efficiently, optimizing the balance between reliability and complexity.
4Reliability
If manual review of all determination basis information is performed, then diagnostic accuracy is maintained, but the diagnosis period extends
Solution Approach 1:
Instead of requiring complete manual review of all determination basis information, the system implements partial review where pathologists verify only critical findings and high-confidence AI results. This partial action approach maintains diagnostic accuracy for essential elements while significantly reducing the overall diagnosis period.
Solution Approach 2:
The system uses feedback mechanisms where AI pre-validates certain findings, allowing pathologists to focus their review on cases where AI confidence is lower or where clinical significance is higher. This feedback-driven selective review maintains accuracy while reducing unnecessary review time.
Data Source
AI summary
Provided is an image diagnostic system that diagnoses a medical image using an artificial intelligence function. The image diagnostic system includes: a control unit; a diagnosis unit that estimates a diagnosis result using a machine learning model on the basis of an input image; a diagnosis result report output unit that outputs a diagnosis result report on the basis of the diagnosis result; a selection unit that selects a part of a diagnostic content included in the diagnostic result report; and an extraction unit that extracts determination basis information that has affected estimation of the diagnosis content selected, in which the control unit outputs the determination basis information. The image diagnostic system further includes a correction unit that corrects the determination basis information, in which the determination basis information corrected is used for relearning of the machine learning model.