Applications of Artificial Intelligence and Machine Learning in Disasters and Public Health Emergencies.

Journal: Disaster medicine and public health preparedness
Published Date: Aug 1, 2022

Abstract

Indexed literature (from 2015 to 2020) on artificial intelligence (AI) technologies and machine learning algorithms (ML) pertaining to disasters and public health emergencies were reviewed. Search strategies were developed and conducted for PubMed and Compendex. Articles that met inclusion criteria were filtered iteratively by title followed by abstract review and full text review. Articles were organized to identify novel approaches and breadth of potential AI applications. A total of 1217 articles were initially retrieved by the search. Upon relevant title review, 1003 articles remained. Following abstract screening, 667 articles remained. Full text review for relevance yielded 202 articles. Articles that met inclusion criteria totaled 56 articles. Those identifying specific roles of AI and ML (17 articles) were grouped by topics highlighting utility of AI and ML in disaster and public health emergency contexts. Development and use of AI and ML have increased dramatically over the past few years. This review discusses and highlights potential contextual applications and limitations of AI and ML in disaster and public health emergency scenarios.

Authors

  • Sally Lu
    Office of Critical Event Preparedness and Response (CEPAR), Johns Hopkins University, Baltimore, MD, USA.
  • Gordon A Christie
    Asymmetric Operations Sector, Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA.
  • Thanh T Nguyen
    School of Information Technology, Deakin University, Waurn Ponds, VIC, Australia.
  • Jeffrey D Freeman
    National Health Mission, Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA.
  • Edbert B Hsu
    Office of Critical Event Preparedness and Response (CEPAR), Johns Hopkins University, Baltimore, MD, USA.

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