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Related Experiment Video

Updated: Sep 11, 2025

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Generative Artificial Intelligence-based Surveillance for Avian Influenza Across a Statewide Healthcare System.

Katherine E Goodman1,2, Seyed M Shams2,3, Laurence S Magder1

  • 1Department of Epidemiology and Public Health, The University of Maryland School of Medicine, Baltimore, Maryland, USA.

Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America
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Generative artificial intelligence rapidly identified patients with potential avian influenza exposure during emergency department visits. This technology offers a cost-effective method for enhancing public health surveillance in clinical settings.

Keywords:
LLMsavian influenzagenerative AIpublic health surveillance

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Area of Science:

  • Public Health
  • Infectious Disease Surveillance
  • Artificial Intelligence in Medicine

Background:

  • Acute respiratory illness and conjunctivitis are common reasons for emergency department visits.
  • Effective surveillance is crucial for early detection of infectious disease outbreaks, such as avian influenza.
  • Traditional surveillance methods can be resource-intensive and time-consuming.

Purpose of the Study:

  • To evaluate the effectiveness of generative artificial intelligence (AI) for public health surveillance.
  • To assess the ability of AI to rapidly and cost-effectively identify patients with potential avian influenza exposure.
  • To explore the utility of AI-assisted surveillance in clinical settings.

Main Methods:

  • A statewide healthcare system's emergency department visit data (n = 13,494) from 2024 was analyzed.
  • Generative artificial intelligence was employed for surveillance of acute respiratory illness and conjunctivitis cases.
  • Adjunctive human review was used to validate AI-identified cases with potential avian influenza exposure.

Main Results:

  • Generative AI rapidly identified patients with potential avian influenza exposure among emergency department visits.
  • The AI-based surveillance approach was found to be cost-effective.
  • The system successfully flagged cases requiring further investigation for avian influenza.

Conclusions:

  • Generative artificial intelligence demonstrates significant promise for enhancing public health surveillance in clinical settings.
  • AI-based surveillance can complement traditional methods by rapidly identifying at-risk populations.
  • This approach offers a scalable and efficient solution for early detection of potential infectious disease threats like avian influenza.