Artificial intelligence enhances genomic surveillance in healthcare outbreak investigations
The EDS-HAT AI tool significantly improved outbreak investigations by automating the identification of transmission routes, both with concordant findings of manual review as well as finding additional routes of transmission missed by traditional chart review. AI with genomic surveillance has the potential to optimize outbreak detection and investigation to streamline interventions in healthcare settings.
Authors
- Lora Pless (ORCID: https://orcid.org/0000-0003-0163-3689)
- Graham M. Snyder (ORCID: https://orcid.org/0000-0001-5562-8880)
- Kathleen A. Shutt (ORCID: https://orcid.org/0000-0003-3376-6152)
- M. Patrick Griffith (ORCID: https://orcid.org/0000-0003-1012-881X)
- Artur Dubrawski (ORCID: https://orcid.org/0000-0002-2372-0831)
- Alexander Sundermann (ORCID: https://orcid.org/0000-0002-2439-6287)
- Lee H. Harrison (ORCID: https://orcid.org/0000-0002-3787-2705)
- Melissa Saul (ORCID: https://orcid.org/0000-0002-6712-5102)
- Jieshi Chen (ORCID: https://orcid.org/0000-0003-2660-4913)
Institutions
- University of Pittsburgh (US)
- UPMC Presbyterian (US)
- Carnegie Mellon University (US)
Publication Details
- Journal
- Infection Control and Hospital Epidemiology
- Published
- 2025-11-14
- DOI
- https://doi.org/10.1017/ice.2025.10355
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- National Institutes of Health
- National Institute of Allergy and Infectious Diseases