This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1580) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1944) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1151) Outbreak and Pandemic Preparedness and Management (1839) Natural Language Processing (937) Artificial Intelligence (2251) Machine Learning (2171)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65226, first published .
Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study

Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study

Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study

Authors of this article:

Michael S Deiner1, 2 Author Orcid Image ;   Russell Y Deiner3 Author Orcid Image ;   Cherie Fathy4 Author Orcid Image ;   Natalie A Deiner5 Author Orcid Image ;   Vagelis Hristidis6 Author Orcid Image ;   Stephen D McLeod1, 2, 7 Author Orcid Image ;   Thomas J Bukowski1 Author Orcid Image ;   Thuy Doan1, 2 Author Orcid Image ;   Gerami D Seitzman1, 2 Author Orcid Image ;   Thomas M Lietman1, 2, 8 Author Orcid Image ;   Travis C Porco1, 2, 8 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Kaur J, Butt Z. AI-driven epidemic intelligence: the future of outbreak detection and response. Frontiers in Artificial Intelligence 2025;8 View
  2. Chen P, Gu H, Lo H, Chang W, Lai C, Lai S, Cheng A, Ng P. Leveraging Large Language Models for Early Detection of Anomaly Work Injury Cases: Data-Driven Approach to Rehabilitation Efficiency. JMIR Rehabilitation and Assistive Technologies 2026;13:e80607 View