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Embeddings of clinical codes enable knowledge-grounded AI in medicine.

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Title: Embeddings of clinical codes enable knowledge-grounded AI in medicine.
Authors: Johnson R; The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA.; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Gottlieb U; Clalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel., Shaham G; Clalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel., Eisen L; Clalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel., Waxman J; Clalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel., Devons-Sberro S; Clalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel., Ginder CR; Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Hong P; Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.; Information Technology, Enterprise Data Analytics and Reporting, Boston Children's Hospital, Boston, MA, USA.; Department of Pediatrics, Harvard Medical School, Boston, MA, USA., Sayeed R; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Su X; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Reis BY; The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA.; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.; Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.; Harvard Data Science Initiative, Cambridge, MA, USA., Balicer RD; The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA.; Clalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel.; Faculty of Health Sciences, School of Public Health, Ben Gurion University, Be'er Sheva, Israel., Dagan N; The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA. noada@clalit.org.il.; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. noada@clalit.org.il.; Clalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel. noada@clalit.org.il.; Faculty of Computer and Information Science, Ben Gurion University, Be'er Sheva, Israel. noada@clalit.org.il., Zitnik M; The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA. marinka@hms.harvard.edu.; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. marinka@hms.harvard.edu.; Harvard Data Science Initiative, Cambridge, MA, USA. marinka@hms.harvard.edu.; Broad Institute of MIT and Harvard, Cambridge, MA, USA. marinka@hms.harvard.edu.; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA, USA. marinka@hms.harvard.edu.
Source: NPJ digital medicine [NPJ Digit Med] 2026 Jun 11. Date of Electronic Publication: 2026 Jun 11.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101731738 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2398-6352 (Electronic) Linking ISSN: 23986352 NLM ISO Abbreviation: NPJ Digit Med
Database: MEDLINE Ultimate
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