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Failure detection in automated image classification is a critical safeguardfor clinical deployment. Detected failure cases can be referred to humanassessment, ensuring patient safety in computer-aided clinical decision making.Despite its paramount importance, there is insufficient evidence about theability of state-of-the-art confidence scoring methods to detect test-timefailures of classification models in the context of medical imaging. This paperprovides a reality check, establishing the performance of in-domainmisclassification detection methods, benchmarking 9 widely used confidencescores on 6 medical imaging datasets with different imaging modalities, inmulticlass and binary classification settings. Our experiments show that theproblem of failure detection is far from being solved. We found that none ofthe benchmarked advanced methods proposed in the computer vision and machinelearning literature can consistently outperform a simple softmax baseline,demonstrating that improved out-of-distribution detection or model calibrationdo not necessarily translate to improved in-domain misclassification detection.Our developed testbed facilitates future work in this important area

Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed