Home

Explore Our Department

Welcome to the Department of Biomedical Data Science

The Department of Biomedical Data Science merges the disciplines of biomedical informatics, biostatistics, computer science and advances in AI. The intersection of these disciplines is applied to precision health, leveraging data across the entire medical spectrum, including molecular, tissue, medical imaging, EHR, biosensory and population data.

Read the DBDS 2024 Annual Report:

What we’re doing, what we’re planning,

and what our future looks like.

For an ADA compliant version
of the Annual Report, please click here.
Cover image for annual report

Open Line, Open Rank, Faculty Cluster Hire Search
Stanford University School of Medicine

The Stanford University School of Medicine (SoM) is recruiting multiple faculty at the Assistant, Associate, or Full Professor in the University Tenure Line (UTL), University Medical Line (UML), or Non-Tenure Line-Research (NTL-R) through this AI (Artificial Intelligence) Faculty Cluster Hire Search. We are specifically interested in candidates who have experience developing and applying novel biomedical AI and data science methods that incorporate biomedical domain expertise to ensure relevance and impact to health and medicine. Candidates will be hired into one or more SoM department(s) and contribute to the research, educational, and if relevant, clinical activities.

For more information and to apply, please click here. 

Our Mission

The Department of Biomedical Data Science (DBDS) is an academic research community, comprised of faculty, students, and staff, whose mission is to advance precision health by leveraging large, complex, multi-scale real-world data through the development and implementation of novel analytical tools and methods.

What is Biomedical Data Science?

Biomedical Data Science “spans a range of biological and medical research challenges that are data intensive and focused on the creation of novel methodologies to advance biomedical science discovery.” The term “data science” describes expertise associated with taking (usually large) data sets and annotating, cleaning, organizing, storing, and analyzing them for the purposes of extracting knowledge. It merges the disciplines of statistics, computer science, and computational engineering” (Annual Review of Biomedical Data Science).

@StanfordDBDS

James Zou in StatNews: AI agents in health care: Everything you need to know, but didn’t know how to ask

AI agents differ from earlier generations of AI tools, but hurdles stand in the way of wider adoption

Several health tech companies have jumped in on agents. One of the early entrants, Hippocratic AI, has a lineup of AI “nurses” that health systems can deploy for specific workflows, such as discussing cervical cancer screening or checking in on a chronic kidney disease patient’s ongoing care. The company also has agents aimed at insurers and drugmakers.

Read more: https://www.statnews.com/2025/04/09/ai-agents-gain-foothold-health-care-industry-but-issues-remain-safety-reliability/

Biomedical Data Science (DBDS) Graduate Program

Our mission is to train future research leaders to design and implement novel quantitative and computational methods that solve challenging problems across the entire spectrum of biology and medicine.