SUDO: a framework for evaluating clinical artificial intelligence systems without ground-truth annotations
This addresses the challenge of ensuring trust in AI predictions for medical applications when ground truth is unavailable, which is crucial for ethical deployment in healthcare, though it is an incremental improvement on existing evaluation methods.
The paper tackles the problem of evaluating clinical AI systems on new data without ground-truth annotations, especially under distribution shift, by introducing the SUDO framework, which uses temporary labels and model training to identify unreliable predictions and assess algorithmic bias, showing it can be a reliable proxy for performance across dermatology, histopathology, and clinical report datasets.
A clinical artificial intelligence (AI) system is often validated on a held-out set of data which it has not been exposed to before (e.g., data from a different hospital with a distinct electronic health record system). This evaluation process is meant to mimic the deployment of an AI system on data in the wild; those which are currently unseen by the system yet are expected to be encountered in a clinical setting. However, when data in the wild differ from the held-out set of data, a phenomenon referred to as distribution shift, and lack ground-truth annotations, it becomes unclear the extent to which AI-based findings can be trusted on data in the wild. Here, we introduce SUDO, a framework for evaluating AI systems without ground-truth annotations. SUDO assigns temporary labels to data points in the wild and directly uses them to train distinct models, with the highest performing model indicative of the most likely label. Through experiments with AI systems developed for dermatology images, histopathology patches, and clinical reports, we show that SUDO can be a reliable proxy for model performance and thus identify unreliable predictions. We also demonstrate that SUDO informs the selection of models and allows for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. The ability to triage unreliable predictions for further inspection and assess the algorithmic bias of AI systems can improve the integrity of research findings and contribute to the deployment of ethical AI systems in medicine.