Health Technologies:
Genomic AST: AI Platform for Same-Day Antibiotic Decisions
David Greenberg, M.D.
- Professor of Internal Medicine and Microbiology
- Director, Microbial Genomics, Division of Infectious Diseases and Geographic Medicine, UT Southwestern Medical Center
This Discovery Track is building a “virtual AST” platform that uses the bacteria’s own DNA and methylation patterns, combined with advanced AI, to tell doctors which antibiotics will work for a given infection. Instead of waiting 2–5 days for traditional culture-based tests, the goal is a same-day resistance report from sequencing data alone, which is especially critical for cancer, transplant, and other high-risk patients. The team has already demonstrated in large retrospective datasets that their PARP model can accurately predict resistance across many pathogens and drugs, providing strong scientific proof-of-concept and clear technical differentiation. The next step is to harden this into a secure, cloud-ready pipeline that can plug into UTSW’s clinical and Core infrastructure and be piloted in real-world care pathways. If successful, this becomes a scalable, pathogen-agnostic engine for precision antibiotic decisions, with attractive recurring service revenue and IP potential around methylation-informed resistance prediction.
