Skip to main content

News

panel
Source:
UC Berkeley CDSS
May 6, 2025

The Frontiers of Computational Health conference gathered more than 150 academic and industry researchers, staff and students from disciplines across varied health specialties, computer science, en

jama internal medicine logo
Source:
JAMA Internal Medicine
May 5, 2025

Chris Williams, Ben Rosner, et al publish in JAMA Internal Medicine - the study evaluated the quality and safety of discharge summary narratives generated by a large language model (GPT-4

poster session
Source:
UCSF School of Medicine
May 2, 2025

Collaboration is a driving force behind UCSF’s research mission – uniting experts across disciplines, institutions, and sectors to solve complex problems.

atul butte AI panel
Source:
UCSF
April 30, 2025

Pioneering bioinformatics expert and BCHSI founding director Atul Butte is elected to the American Academy of Arts and Sciences.

chart
Source:
JAMA Open Network
April 24, 2025

From Hong Lab - led by Chichi Chang, the team used natural language processing to evaluate symptoms experienced by >28k patients with cancer prior to emergency visits and hospitalizations.

travis zack
Source:
Health IT News
April 17, 2025

BCHSI affiliated faculty member Travis Zack shares how the implementation of AI in oncology workflows has led to measurable improvements in efficiency and decision making in oncology.

Matrix inversion and subset selection (MISS)
Source:
Nature Communications Biology
March 11, 2025

Justin Torok, Pedro Maia, Anand Chaitaili and Ashish Raj recent publish in Nature Communication Biology.

cell
Source:
Cell Reports
February 25, 2025

Latest publication in Cell by Grace Ramey, Marina Sirota, Alice Tang, et al.

MRI
Source:
UCSF
February 7, 2025

Work featuring BCHSI affiliated faculty Reza Abbasi-Asl and Geoff Tison.

nejm ai
Source:
NEJM AI
January 23, 2025

Jean Feng,  Romain Pirracchio, et al publication.

jco
Source:
JCO Clinical Informatics
January 14, 2025

Julian Hong, Travis Zack, Atul Butte et al publish on the effectiveness of proprietary and open large language models (LLMs) in detecting disease presence, location, and treatment response in pancr