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affiliated faculty Christopher Y. K. Williams, Travis Zack,  Brenda Y. Miao, et al publish - LLMs may be able to accurately identify higher acuity patient presentation when given pairs of presenting histories extracted from patients’ first ED documentation

Pre-training a generative selective state space model, the Mamba model, on UCSF-specific deidentified clinical notes and time-series structured data - Madhumita Sushil , UCSF
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Andreas Rauschecker, MD, PhD, Assistant Professor of Radiology, BCHSI-affiliated faculty, recipient for: Deep Learning Radiogenomic Analysis of Primary CNS Lymphoma on Brain MRI

For cardiologist Rima Arnaout, MD, imaging is the “final frontier” of phenotyping. AI’s ability to analyze images could help echocardiographers like her rule out diseases and abnormalities.

Study by Izzy Freisner, Bakar affiliated faculty Jean Feng, Julian Hong, et al.

Bakar affiliated faculty Julian Hong, et al publish on cost study of their randomized controlled healthcare Machine Learning trial, ML directed supportive care to reduce hospitalizations and ER visits during radiation treatment cost-efficiently

Madhumita Sushil, Travis Zack, Atul Butte, et al publish in NEJM AI on releasing a new oncology NLP benchmark of 40 breast and pancreatic cancer progress notes from UCSF, comprehensively annotated by medical experts.

BCHSI affiliated faculty Ida Sim makes list of “50 influential people shaping the future of health and life sciences.”

Alice Tang, lead author in Sirota lab, recently published in Nature Aging.

Dr. Atul Butte, a pediatric physician at UCSF and the chief data scientist for the University of California Health System, said AI tools can help pinpoint what other drugs or treatment are necessary for the countless cancer variations and mutations.

BCHSI faculty Julia Adler Milstein, et al explore public comfort with the use of different data types for predictive models