This technology is a method for quantifying the quality of artificial intelligence models and standardizing their application in clinical settings.
Medical diagnostic errors lead to millions of outpatient misdiagnoses annually in the U.S. Artificial intelligence (AI)-derived tools can support clinicians by facilitating rapid and accurate diagnoses. However, there is a lack of standardization and methodology to evaluate if AI models meet specific robustness criteria and corroborate human experts.
This technology introduces a method for quantifying the robustness and interpretability of deep learning models in order to standardize the application of AI-derived tools in clinical settings. It introduces convolutional neural networks (CNN) that robustly detect glaucoma from optical coherence tomography (OCT) images. Quantitative interpretability scores are generated for certain image features and compared to eye fixations of skilled medical professionals, identifying image features used by both human experts and AI. Since this technology provides a method for assessing AI interpretability based on expert feedback, it has the potential to significantly improve disease diagnosis in various medical disciplines.
IR CU21011
Licensing Contact: Dovina Qu
