In a groundbreaking advancement at the crossroads of artificial intelligence and epidemiology, researchers at the University of Maryland School of Medicine have unveiled a novel application of generative AI to bolster surveillance efforts against H5N1 avian influenza—a virus with a notorious potential for widespread outbreaks. By leveraging the power of large language models (LLMs) to comb through voluminous electronic medical records (EMRs), this innovative approach identifies high-risk patients harboring possible bird flu infections, many of whom might otherwise elude detection during routine clinical assessments.
The research centered on an analysis of 13,494 emergency department visits spanning urban, suburban, and rural hospitals within the University of Maryland Medical System (UMMS) in 2024. Patients included were those presenting symptoms consistent with early avian influenza infection: acute respiratory issues such as coughs, fevers, nasal congestion, and conjunctivitis. By deploying GPT-4 Turbo, a state-of-the-art generative AI, the team systematically parsed clinical notes, pinpointing subtle references to animal exposure—a critical risk factor in zoonotic transmission of H5N1.
Remarkably, the AI flagged 76 clinical records that contained annotations related to high-risk bird flu exposures. These mentions were often buried incidentally within patients’ occupational or environmental histories—for example, noting a patient’s work as a butcher or engagement on a livestock farm. Such incidental documentation rarely triggers suspicion of avian influenza during real-time clinical decision-making, underscoring the potential blind spots in conventional surveillance that AI is uniquely positioned to address.
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.
Currently, the Centers for Disease Control and Prevention (CDC) relies heavily on mandated laboratory reporting to track avian influenza cases. However, the absence of systems monitoring clinicians’ documentation practices leaves a critical blind spot in understanding how thoroughly potential exposures are assessed and recorded. The University of Maryland team’s AI tool offers a transformative solution by filling this documentation gap and enhancing disease surveillance granularity.
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
