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

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.

Clinical characteristics and risk factors for mortality in COVID-19 inpatients in Birjand, Iran: a single-center retrospective study

“…In return, some patients appear with a fast progression, like respiratory failure or even death [ 14 ]. Also, the fatality rate significantly increased in cardiovascular patients and also patients with hypertension and diabetes [ 16 ]. Mortality increases mostly with the progression of the disease.…”

Brave new world: Leveraging artificial intelligence for advancing healthcare epidemiology, infection prevention, and antimicrobial stewardship

“…An automated national system leveraging AI capabilities and incorporating individuallevel information could address these limitations and provide a Public health Identify false or misleading information by comparing to reputable sources of information. Synthesize and summarize literature to support optimal decision making 4,23 Improve disease surveillance, outbreak detection and efficiency of disaster response, and resource allocation 25,26,32 more accurate and comprehensive understanding of outpatient antibiotic usage across healthcare systems in the United States and globally. AI may also support diagnostic stewardship and assist with validation of new molecular methodologies in the clinical microbiology laboratory that are essential to evaluating the accuracy and reliability of these tests before implementation in clinical care.…”

“…25 This approach ensures that aid is delivered to the individuals who need it most, improving response times and reducing waste. 26 The CDC uses AI for its Syndromic Surveillance System, which collects and analyzes emergency department data in real time to detect potential outbreaks. 27 It was used heavily during the 2022-2023 "triple-demic" of COVID-19, influenza, and RSV.…”

“…25 This approach ensures that aid is delivered to the individuals who need it most, improving response times and reducing waste. 26…”

Revolutionizing antimicrobial stewardship, infection prevention, and public health with artificial intelligence: the middle path

“… 13 Additionally, at the population level, AI optimizes the delivery of humanitarian aid by analyzing data on population density, infrastructure, and resources availability, ensuring aid reaches the most affected areas promptly. 14 …”