Integrating clinical knowledge into AI remains challenging despite established medical guidelines and vocabularies. Medical codes, central to healthcare systems, often reflect operational patterns shaped by geographic factors, national policies, insurance frameworks, and physician practices rather than the precise representation of clinical knowledge. This disconnect hampers AI in representing clinical relationships, raising concerns about bias, transparency, and generalizability. Here, we developed a resource of 67,124 clinical vocabulary embeddings derived from a clinical knowledge graph tailored to electronic health record vocabularies, spanning over 1.3 million edges. Using graph transformer neural networks, we generated clinical vocabulary embeddings that provide a new representation of clinical knowledge unified across seven medical vocabularies. We validated these embeddings through a phenotype risk score analysis involving 4.57 million patients from Clalit Healthcare Services, demonstrating their ability to stratify individuals by survival outcomes. Inter-institutional panels of clinicians evaluated the alignment of embedding with established clinical knowledge across 90 diseases and 3,000 clinical codes, confirming their robustness and transferability. This resource addresses the gap in integrating clinical vocabularies in AI models and training datasets and supports population and patient models in precision medicine.