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Subject: search.filters.subject.eXplainable AI (XAI)

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    Item type:Publication,
    Comparing AI Decision-Making with Expert Biomarkers: A Case Study on Diabetic Retinopathy Classification
    (2025-01-01)
    Sharma, Aayushma
    ;
    Paing, May Phu
    Artificial intelligence (AI) has become prevalent in the healthcare sector due to its ability to interpret complex medical images that may not be apparent to humans. Traditional black-box models were used to classify the disease, providing no information as to why certain things were labeled as such and not others. This paper utilizes the use of eXplainable-AI (XAI), specifically, Layer-wise Relevance Propagation (LRP) which generates mapping between AI decision and biomarker used by the ophthalmologist whereby enhancing results interpretability and transparency in the disease diagnostic tasks. VGG-16 incorporated with batch normalization and label smoothing was used for the classification tasks whereas LRP was employed to perform the heat-map generation to see if the feature extracted and used by AI was consistent with the experts' biomarkers. Our proposed model obtained a classification accuracy of 77.33%, where 165 out of 266 images were aligned with the ophthalmologist's prediction. Furthermore, the significance of heatmap generation was supported by a one-sample Z-test which revealed that the alignment between AI predictions and expert biomarkers is significantly greater than random, with a 95% confidence interval.