Pathology Image Uncertainty Estimation for AI Fallback Decisions
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Solution Overview
Problem
Existing AI systems in medicine may provide erroneous predictions due to their training on pre-existing test outcomes as 'ground truth', leading to potential failures and higher error rates compared to traditional tests, necessitating a method to determine when AI systems should fallback to traditional methods.
Innovation Solution
Implementing uncertainty estimation methods using open-set classifiers and local feature consistency analysis to identify subpopulations where AI accuracy is low, triggering a fallback to traditional tests, thereby ensuring accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI systems are trained on pre-existing test outcomes as ground truth, then AI prediction speed and efficiency are improved, but AI accuracy and reliability deteriorate due to error propagation from traditional tests
Solution Approach 1:
The patent introduces an uncertainty estimation module as an intermediary between the AI prediction system and the final diagnosis. This module evaluates the reliability of AI predictions by analyzing feature consistency and comparing multiple predictions, acting as a mediator that determines when to trust the AI system and when to fallback to traditional testing methods.
Solution Approach 2:
The system implements feedback loops where uncertainty estimates from the AI system are fed back into the decision-making process. When uncertainty exceeds thresholds, the system triggers traditional testing, and the results are used to refine future AI predictions, creating a continuous improvement cycle that addresses accuracy issues.
2Productivity
If AI systems are used for all pathology specimens, then productivity and cost-effectiveness are improved, but error rates increase for certain subpopulations outside training data distribution
Solution Approach 1:
The patent performs preliminary uncertainty assessment before final AI prediction is accepted. By evaluating feature consistency and comparing multiple predictions in advance, the system identifies specimens that may fall outside training data distribution before making final diagnostic decisions, allowing for proactive fallback to traditional methods when needed.
Solution Approach 2:
The system dynamically adjusts its reliance on AI predictions based on real-time uncertainty estimates. For specimens with high feature consistency and low uncertainty, the system confidently uses AI predictions. For specimens with low feature consistency or high uncertainty, the system dynamically switches to traditional testing methods, creating a flexible hybrid approach.
3Reliability
If uncertainty estimation and fallback mechanisms are implemented, then AI prediction reliability is improved, but system complexity and processing time increase
Solution Approach 1:
The patent segments the uncertainty estimation process into distinct modular components: feature extraction modules, consistency evaluation modules, and fallback decision modules. This segmentation allows each component to be independently optimized and maintained, reducing overall system complexity while preserving reliability benefits.
Solution Approach 2:
The system discards AI predictions when uncertainty exceeds thresholds and recovers by using traditional testing methods instead. This selective discarding and recovery approach prevents the propagation of unreliable predictions while maintaining system simplicity by only activating additional complexity when necessary.
4Reliability
If traditional tests are used instead of AI systems, then prediction accuracy is improved for uncertain cases, but processing time and cost increase
Solution Approach 1:
The patent changes the operational parameters of the testing system by dynamically selecting between AI and traditional methods based on uncertainty estimates. For low-uncertainty cases, the system uses rapid AI processing with short turnaround time. For high-uncertainty cases, it switches to traditional methods, optimizing the overall time-quality tradeoff across the entire workflow.
Data Source
AI summary
A method for processing electronic images using uncertainty estimation may be used to determine whether to use an artificial intelligence (AI) assisted prediction. The method may include receiving one or more electronic images associated with a pathology specimen and providing the one or more electronic images to a machine learning model. The machine learning model may perform operations including determining a certainty level corresponding to a certainty that a predetermined AI system will provide an accurate prediction, determining whether the certainty level equals or exceeds a predetermined confidence threshold, and, upon determining that the certainty level does not equal or exceed a predetermined confidence threshold, determining to not use the predetermined AI system.