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Faculty in the Department of Biomedical Informatics & Data Science at Yale School of Medicine received $570,000 in new grants and contracts in support of real-world evidence research and clinical data science.

On April 13, Yale Biomedical Informatics and Computing's Research Informatics Office (RIO) hosted the inaugural Yale Cosmos Datathon at 101 College Street.

Members from the Yale University were honored at the 2026 Translational Science meeting by the Association for Clinical and Translational Science.

Two trainees from the Department of Biomedical Informatics & Data Science at Yale School of Medicine have been named recipients of the 2026 NSF Graduate Research Fellowship Program (GRFP) award.

On March 26, 2026, the Yale Center for Clinical Investigation (YCCI) and the Yale Biomedical Informatics and Computing (YBIC) Core co-hosted the inaugural 2026 Yale Medical AI Symposium at 101 College Street — home to the Department of Biomedical Informatics and Data Science (BIDS) — bringing together clinicians, researchers, trainees, and CTSA network partners to advance responsible AI in medicine.

On Friday, March 20th, the Precision Medicine in Cancer Program at Yale Cancer Center hosted a day long workshop focused on real-world applications of AI across the cancer care continuum, from bench to clinical implementation to population health.

The Yale Medical AI Symposium will bring together experts in and outside of Yale to discuss best practices and research collaborations.

The new Human Genome Sciences track in the Biological & Biomedical Sciences (BBS) PhD program, open for applications this fall, is an interdisciplinary initiative at the intersection of human genomics, genetics, and informatics.

Lucila Ohno-Machado, MD, MBA, PhD, and Tsung-Ting Kuo, PhD, have been awarded a four-year, $2.7 million grant from the National Library of Medicine to develop novel methods for explaining clinical AI models. The project, "Supervised Weighted Distances: Patient Similarity to Explain Clinical AI Models," will create algorithms and interactive visualization tools that help clinicians understand AI predictions by showing how individual patients compare to similar past cases with known outcomes. Running from September 2025 through August 2029, the research aims to increase trust and adoption of AI in clinical settings by making machine learning recommendations more transparent and interpretable for healthcare providers.