🤖 AI Summary
This work addresses the neglect of ground-truth uncertainty in medical AI evaluation—particularly in dermatology, where diagnoses often manifest as differential diagnoses with inter-expert disagreement. We propose an uncertainty-aware evaluation framework that models annotation aggregation as posterior inference, uniquely disentangling ground-truth uncertainty into two components: annotator reliability (annotation uncertainty) and intrinsic disease ambiguity (inherent uncertainty). Building upon plausibility distributions, we formulate an uncertainty-aware evaluation paradigm and introduce novel calibration metrics. Our method integrates a probabilistic extension of the Plackett–Luce model with inverse rank normalization (IRN), enabling interpretable uncertainty quantification. Empirical validation on skin image classification reveals substantial ground-truth uncertainty across many samples; conventional IRN markedly overestimates model performance, whereas our framework accurately identifies high-risk predictions and delivers calibrated, uncertainty-aware assessment.
📝 Abstract
For safety, AI systems in health undergo thorough evaluations before deployment, validating their predictions against a ground truth that is assumed certain. However, this is actually not the case and the ground truth may be uncertain. Unfortunately, this is largely ignored in standard evaluation of AI models but can have severe consequences such as overestimating the future performance. To avoid this, we measure the effects of ground truth uncertainty, which we assume decomposes into two main components: annotation uncertainty which stems from the lack of reliable annotations, and inherent uncertainty due to limited observational information. This ground truth uncertainty is ignored when estimating the ground truth by deterministically aggregating annotations, e.g., by majority voting or averaging. In contrast, we propose a framework where aggregation is done using a statistical model. Specifically, we frame aggregation of annotations as posterior inference of so-called plausibilities, representing distributions over classes in a classification setting, subject to a hyper-parameter encoding annotator reliability. Based on this model, we propose a metric for measuring annotation uncertainty and provide uncertainty-adjusted metrics for performance evaluation. We present a case study applying our framework to skin condition classification from images where annotations are provided in the form of differential diagnoses. The deterministic adjudication process called inverse rank normalization (IRN) from previous work ignores ground truth uncertainty in evaluation. Instead, we present two alternative statistical models: a probabilistic version of IRN and a Plackett-Luce-based model. We find that a large portion of the dataset exhibits significant ground truth uncertainty and standard IRN-based evaluation severely over-estimates performance without providing uncertainty estimates.
