CLEAR: an auditable foundation model for radiology grounded in clinical concepts.

'Black box' deep learning models for medical image interpretation limit clinical trust and analysis of performance degradation. Here we introduce Concept-Level Embeddings for Auditable Radiology (CLEAR), an auditable foundation model based on clinical concepts. Trained on over 0.87 million image-report pairs from 239,391 patients, CLEAR learns a visual representation and projects chest X-rays into a semantically rich space defined by large language model embeddings, making every prediction decomposable into weighted contributions from individual radiological observations. External validation on four large, physician-annotated datasets from the United States, Europe and Asia shows that CLEAR not only achieves state-of-the-art classification performance but also enables applications: auditable zero-shot pathology detection, systematic identification of radiological confounders and the creation of expert-level concept bottleneck models from data-driven concepts. By integrating clinical knowledge directly into its reasoning process, CLEAR offers a framework for robust model auditing, safer deployment and enhanced physician-AI collaboration, advancing towards trustworthy medical AI.