Generative AI Reveals Hidden Bird Flu Exposure Risks in Maryland Emergency
NEWS
EXPLORE
CAREER
Companies
Jobs
EVENTS
iGEM
News
Team
PHOTOS
VIDEO
WIKI
BLOG
COMMUNITY
Friday, April 24, 2026
No Result
View All Result
NEWS
EXPLORE
CAREER
Companies
Jobs
Lecturer
PhD Studentship
Postdoc
Research Assistant
EVENTS
iGEM
News
Team
PHOTOS
VIDEO
WIKI
BLOG
COMMUNITY
NEWS
EXPLORE
CAREER
Companies
Jobs
Lecturer
PhD Studentship
Postdoc
Research Assistant
EVENTS
iGEM
News
Team
PHOTOS
VIDEO
WIKI
BLOG
COMMUNITY
No Result
View All Result
No Result
View All Result
NEWS
Science News
Health
Generative AI Reveals Hidden Bird Flu Exposure Risks in Maryland Emergency Departments
September 6, 2025
in
Health
Reading Time: 3 mins read
Share on Facebook
Share on Twitter
Share on Linkedin
Share on Reddit
Share on Telegram
Following AI flagging, human research staff conducted a brief review, confirming 14 instances of recent exposure to animals commonly associated with H5N1, including poultry, wild birds, and other livestock. These patients had not been tested specifically for the virus, highlighting a critical surveillance gap; infections might have been missed due to lack of suspicion or targeted diagnostic testing. This “needle in a haystack” detection demonstrates the power of AI algorithms not only to augment but to revolutionize infectious disease surveillance in hospital systems.
Katherine E. Goodman, PhD, JD, the study’s corresponding author and an Assistant Professor of Epidemiology & Public Health, emphasized the immense public health implications. She noted that despite H5N1’s ongoing circulation within U.S. animal populations, human cases remain scarce largely because of undetected exposures and insufficient testing regimes. “Because we are not systematically tracking symptomatic patients for potential bird flu exposures, and how many are being tested, many infections could be flying under the radar,” Dr. Goodman remarked. “Integrating AI into surveillance could fill this critical knowledge gap.”
The scale and efficiency of this AI-assisted review were also notable. Anthony Harris, MD, MPH, Professor and Acting Chair at UMSOM, reported that human evaluation of the AI-flagged cases took only 26 minutes total and cost a mere three cents per patient note analyzed. Such scalability suggests feasibility for nationwide deployment across sentinel clinical sites to monitor emerging infectious diseases in real-time, greatly enhancing the agility of public health responses.
Performance metrics from a historical validation set comprising 10,000 emergency department visits from 2022-2023—before the recent bird flu outbreaks—demonstrated the model’s robustness. The LLM achieved a 90% positive predictive value and a 98% negative predictive value for identifying animal exposure mentions. While the model was deliberately conservative to avoid false alarms, occasionally flagging low-risk animal contacts such as with dogs, this underscored the indispensable role of human expertise in final adjudication of flagged cases.
The implications extend beyond retrospective analysis. This methodology’s potential integration into clinical workflows could enable prospective, real-time alerts to healthcare providers. By prompting clinicians to inquire about known high-risk exposures during patient intake, ordering appropriate testing, and enacting infection control protocols such as isolation, the AI model could dramatically reduce missed cases and interrupt transmission chains before escalating outbreaks.
With over 1,075 dairy herds and hundreds of millions of poultry and wild birds already affected by H5N1 since early 2024, the risk of spillover into the human population remains an urgent concern. Although confirmed human cases remain rare—with only 70 infections and a single fatality reported by mid-2025—the absence of widespread testing suggests these numbers likely underrepresent reality. Furthermore, genetic shifts in H5N1 strains could facilitate human-to-human transmission, sharply accelerating the threat landscape.
The University of Maryland Institute for Health Computing (UM-IHC), a collaborative hub combining expertise from the University’s College Park and Baltimore campuses along with the University of Maryland Medical System, orchestrated the computational and clinical integration vital for this research. Access to comprehensive, secure medical records from over two million patients served as a unique and powerful resource, enabling the development and validation of such AI surveillance tools in a real-world healthcare ecosystem.
Mark T. Gladwin, MD, Dean of the School of Medicine and Vice President for Medical Affairs at the University of Maryland, framed this endeavor within the broader revolution of big data and AI in medicine. “We stand at the forefront of a disruptive yet profoundly promising frontier where data-driven insights can be harnessed to detect emerging infectious diseases earlier, respond faster, and ultimately save lives,” he stated, highlighting the potential for similar AI-driven models to reshape public health strategies on a national scale.
Looking ahead, the researchers aim to pilot prospective deployment of the LLM within electronic health record systems to facilitate real-time identification and intervention. As the respiratory virus season reemerges in the fall, having an automated, rapid, and accurate mechanism to detect probable bird flu exposures will be crucial in guiding targeted testing, treatment, and isolation, preventing escalation of outbreaks in clinical and community settings.
This study not only exemplifies an innovative fusion of AI and epidemiology but also illustrates a scalable and cost-effective pathway to enhance infectious disease surveillance infrastructure. By illuminating previously hidden epidemiological signals, generative AI models stand to empower healthcare systems to anticipate and mitigate epidemic threats with unprecedented precision and speed.
Subject of Research
: People
Article Title
: Generative Artificial Intelligence–based Surveillance for Avian Influenza Across a Statewide Healthcare System
News Publication Date
: 13-Aug-2025
Web References
Clinical Infectious Diseases article
CDC Bird Flu Situation Summary
References
Goodman KE, Harris A, Magder LS, Baghdadi JD, Morgan DJ. Generative Artificial Intelligence–based Surveillance for Avian Influenza Across a Statewide Healthcare System. Clin Infect Dis. Published 13 August 2025. doi:10.1093/cid/ciaf369
Image Credits
: University of Maryland School of Medicine
Keywords
: Influenza, Pandemic influenza, Epidemiology, Infectious diseases
Tags: AI-driven healthcare innovationsbird flu surveillance technologyelectronic medical records analysisemergency department patient assessmentGenerative AI in epidemiologyGPT-4 Turbo in medical researchH5N1 avian influenza detectionhigh-risk patient identificationimproving public health surveillance.occupational exposure to avian influenzaUniversity of Maryland School of Medicine researchzoonotic disease transmission
Share
14
Tweet
Share
Share
Share
Share
Related
Posts
Yōni.Fit® Bladder Support for Stress Urinary Incontinence Expands Indication to Include Menstrual Health
April 24, 2026
Immune Surveillance Structures Identified in Skin Hair Follicles
April 24, 2026
University of Cincinnati Collaborates with Local Paramedics to Propel Sudden Cardiac Arrest Research
April 24, 2026
Mount Sinai Study Reveals How Early-Life Metal Exposure Influences Brain Development and Behavior Using Baby Teeth and Brain Imaging
April 24, 2026
POPULAR NEWS
Research Indicates Potential Connection Between Prenatal Medication Exposure and Elevated Autism Risk
817 shares
Share
327
Tweet
204
New Study Reveals Plants Can Detect the Sound of Rain
655 shares
Share
262
Tweet
164
Scientists Investigate Possible Connection Between COVID-19 and Increased Lung Cancer Risk
66 shares
Share
26
Tweet
17
Salmonella Haem Blocks Macrophages, Boosts Infection
60 shares
Share
24
Tweet
15
About
We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.
Recent News
New Study Finds Maternal Dairy Intake Within Guidelines Linked to Reduced Levels of Certain Human Milk Lipids
RagC Detects β-Hydroxybutyrate Levels to Inhibit mTORC1 Activity and Tumor Progression
Nanoscale Nuclear Organization Revealed by High-Resolution Imaging
Subscribe to Blog via Email
Join 81 other subscribers
Bioengineer.org
© Copyright 2023 All Rights Reserved.
No Result
View All Result
Homepages
Home Page 1
Home Page 2
News
National
Business
Health
Lifestyle
Science
Bioengineer.org
© Copyright 2023 All Rights Reserved.