Generative Artificial Intelligence–based Surveillance for Avian Influenza Across a Statewide Healthcare System
Among all 2024 emergency department visits for acute respiratory illness or conjunctivitis across a statewide healthcare system (n = 13 494), generative artificial intelligence-based surveillance with adjunctive human review rapidly and cost-effectively identified patients with potential avian influenza exposure. Generative artificial intelligence-based approaches may offer a promising opportunity for expanding public health surveillance in clinical settings.
Authors
- Jonathan Baghdadi (ORCID: https://orcid.org/0000-0002-2442-0654)
- Katherine E Goodman (ORCID: https://orcid.org/0000-0003-2851-775X)
- S. Shams (ORCID: https://orcid.org/0009-0000-6085-0902)
- Laurence S. Magder (ORCID: https://orcid.org/0000-0001-9297-1952)
- Anthony D. Harris (ORCID: https://orcid.org/0000-0003-2193-1560)
- Daniel J. Morgan
Institutions
- University of Maryland, Baltimore (US)
- University of Maryland Medical System (US)
- Foundation for the National Institutes of Health (US)
Publication Details
- Journal
- Clinical Infectious Diseases
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1093/cid/ciaf369
- Citations
- 6
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 8.13
Funders
- Agency for Healthcare Research and Quality
- Institute for Advanced Computer Studies, University of Maryland