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

作者：Tianyu Han, Riga Wu, Yu Tian, Firas Khader, Lisa C. Adams, Keno K. Bressem, Christos Davatzikos, Jakob Nikolas Kather, Li Shen, David A. Mankoff, Eduardo Mortani Barbosa, Daniel Truhn · 发表于：Nature Biomedical Engineering · 年份：2026 · DOI：10.1038/s41551-026-01741-4 · 研究领域：Artificial Intelligence in Healthcare and Education、COVID-19 diagnosis using AI、Radiology practices and education

'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.