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Research Article

AI and Machine Learning for Early Detection of Infectious Diseases in the US: Opportunities and Challenges

Authors

  • Adekunle Adeoye Department of Mathematics and Statistics, Georgia State University, 33 Gilmer Street SE, Atlanta, GA 30303, USA
  • Chibuzo Okechukwu Onah J. Mack Robinson College of Business, Georgia State University, 33 Gilmer Street SE, Atlanta, GA 30303, USA
  • Enibokun Theresa Orobator College of Medicine and Veterinary Medicine, University of Edinburgh, United Kingdom
  • Elizabeth Anuoluwa Akintayo Department of Bioengineering and Biomedical Engineering, Wayne State University, 45 Warren Ave, Detroit, MI, USA
  • Jacob Ukpabio Inuaeyen Department of Pharmaceutical and Medicinal Chemistry, University of Nigeria, Enugu, Nigeria
  • Daniel Ojonugwa Umoru Department of Pharmacy, National Hospital Abuja, Nigeria
  • Isaiah Olumeko College of Pharmacy, University of Houston, Texas, USA
  • Gladys Jacob Dick Elmleigh Hospital, Havant, United Kingdom
  • Kelechi Wisdom Elechi School of Biomedical Sciences, University of Texas Health Science Center, San Antonio, USA
  • Ubalaeze Solomon Elechi Department of Radiography and Radiological Sciences, University of Nigeria, Enugu Campus, Nigeria https://orcid.org/0009-0002-3474-1002

    elechiuba@gmail.com

  • Muhammad Bello Demola Department of Computer Science and Technology, Ulster University, Birmingham, United Kingdom

Abstract

Artificial intelligence and machine learning are reshaping U.S. infectious-disease surveillance by rapidly integrating clinical, environmental, and open-source data to flag anomalies sooner than conventional methods. This article aims to assess how artificial intelligence (AI) and machine learning (ML) can accelerate early detection of emerging infectious diseases in the United States In case studies, real-time ML tools cut hospital-acquired infections by 40 %, and systems like BlueDot predicted major outbreaks days before official alerts, underscoring strong gains in speed and accuracy. However, biased training data, opaque “black-box” models, privacy risks, and high implementation costs still threaten equitable, trustworthy deployment. Overcoming these barriers will require rigorous data-quality standards, explainable algorithms, interdisciplinary governance, and scalable validation frameworks—advances that could extend early-warning capacity from local hospitals to global health security networks.

Keywords:

AI-Driven Disease Surveillance Computational Epidemiology Health Informatics Smart Public Health Systems Digital Epidemiology Tools

Article information

Journal

Journal of Medical Science, Biology, and Chemistry

Volume (Issue)

2(1), (2025)

Pages

54-63

Published

03-05-2025

How to Cite

Adeoye, A., Onah, C. O., Orobator, E. T., Akintayo, E. A., Inuaeyen, J. U., Umoru, D. O., Olumeko, I., Dick, G. J., Elechi, K. W., Elechi, U. S., & Demola, M. B. (2025). AI and Machine Learning for Early Detection of Infectious Diseases in the US: Opportunities and Challenges. Journal of Medical Science, Biology, and Chemistry, 2(1), 54-63. https://doi.org/10.69739/jmsbc.v2i1.465

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