Deferral Model for AI Diagnostic Reliability
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Solution Overview
Problem
Deep learning-based AI systems in safety-critical applications like medical imaging analysis can be unreliable, leading to errors in cases that can be accurately diagnosed by clinicians, making it challenging to determine when to rely on AI models versus human expertise.
Innovation Solution
A deferral model is introduced that learns to decide when to rely on diagnostic AI models and when to defer to a user, such as a clinician, by using confidence scores from pre-existing 'locked' AI models, allowing for improved classification performance without requiring retraining of the AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI systems are used for diagnostic classification, then classification speed and accuracy for many inputs are improved, but reliability deteriorates in safety-critical cases where AI can misclassify inputs that clinicians could correctly diagnose
Solution Approach 1:
A deferral model is introduced as an intermediary component between the diagnostic AI model and the final classification output. This mediator evaluates the AI model's confidence scores and decides whether to trust the AI's classification or defer to human clinician review, thereby resolving the contradiction between AI speed and reliability in safety-critical cases
Solution Approach 2:
The system implements feedback by having the deferral model continuously evaluate the AI model's performance on incoming inputs based on confidence scores. When the deferral model identifies low-confidence predictions, it triggers a feedback loop to involve human clinicians, creating a dynamic system that adapts its reliance on AI based on real-time performance assessment
2Measurement precision
If AI models are used for diagnostic classification, then classification accuracy is improved for many inputs, but device complexity increases due to the need to determine when to rely on AI versus human expertise
Solution Approach 1:
The complex decision-making logic for determining when to rely on AI versus human expertise is extracted into a separate, dedicated deferral model component. This extraction isolates the complexity management function, allowing the main diagnostic AI model to focus on classification accuracy while the deferral model handles the meta-decision of system configuration
Solution Approach 2:
The system is segmented into distinct functional components: the diagnostic AI model for classification, the deferral model for confidence evaluation, and the human clinician review process for low-confidence cases. This segmentation allows each component to be optimized independently and simplifies the overall system architecture despite the added complexity of coordination
3Adaptability or versatility
If existing diagnostic AI models are used without retraining, then adaptability to new domains is improved, but the models cannot be optimized for specific downstream data distributions
Solution Approach 1:
The deferral model serves multiple functions: it evaluates confidence scores from AI models, determines when to defer to human expertise, and adapts the system to different downstream data distributions. This multi-functionality allows a single component to enable both cross-domain adaptability and performance optimization without requiring retraining of the underlying AI models
Solution Approach 2:
The system achieves adaptation to different downstream data distributions by changing the operational parameters of the deferral model rather than retraining the AI models. The deferral model learns to adjust its confidence thresholds and deferral strategies based on the specific characteristics of each downstream domain, enabling optimization through parameter adaptation
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data points using a deferral model that determines whether to classify the data point using an output of one or more diagnostic machine learning models or to defer the data point for classification by one or more users.