Abstract Artificial intelligence for medical imaging is required to be accurate and interpretable to clinicians. However, current multimodal biomedical foundation models often prioritize performance over explainability. Here we present ConceptCLIP, an explainable biomedical foundation model that achieves state-of-the-art diagnostic accuracy while delivering human-interpretable explanations across diverse imaging modalities. We curate MedConcept-23M, a large-scale dataset comprising 23 million biomedical image–text–concept triplets. Leveraging this dataset, we pretrain ConceptCLIP via joint image–text and region–concept alignment for precise and interpretable medical image analysis. Across a large-scale benchmark covering 78 datasets in 10 imaging modalities, ConceptCLIP demonstrates superior diagnostic performance while providing human-understandable explanations. In a clinician user study spanning three modalities, the concept-based explanations provided by ConceptCLIP help clinicians verify model predictions and identify potential errors. As an explainable biomedical foundation model, ConceptCLIP represents a critical milestone towards the widespread clinical adoption of AI, thereby advancing trustworthy AI in medicine. This is a preview of subscription content, access via your institution Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $32.99 / 30 days cancel any time Subscribe to this journal Receive 12 digital issues and online access to articles $119.00 per year only $9.92 per issue Buy this article - Purchase on SpringerLink - Instant access to the full article PDF. USD 39.95 Prices may be subject to local taxes which are calculated during checkout Data availability This study incorporates a total of 79 datasets. Among these, the proposed MedConcept-23M dataset is used for training the ConceptCLIP model. The image part of the pretraining dataset, MedConcept-23M, is open-sourced and can be obtained directly from the publicly available PMC Open Access Subset (PMC-OA). The captions and concepts are available at https://huggingface.co/datasets/JerrryNie/MedConcept-23M. To enable reconstruction of the full dataset, we provide a dataset reconstruction script as part of