Article type
Abstract
Introduction: Emerging infectious diseases (EIDs) pose significant challenges to global public health systems, necessitating innovative approaches for disease management. Artificial intelligence (AI) has emerged as a promising tool in addressing these challenges due to its potential for rapid data analysis and pattern recognition.
Objective: To explore the current available evidence of AI applications in the context of EID management
Methods:
The methodology proposed by JBI in conjunction with PRISMA-ScR framework was used to conduct the review. A 3-step search strategy was used to locate both published and unpublished studies in the English language. The PubMed, Ebscohost, and Virtual Health Library were searched. Search for unpublished studies and conference proceedings was also conducted. The keywords included were “artificial intelligence, emerging diseases, health promotion, health screening, health surveillance, health prediction.”
All identified citations were collated and uploaded to Zotero. Titles and abstracts were screened by 2 independent reviewers to assess them against the inclusion and exclusion criteria using Rayyan, and duplicates were removed. Disagreements were resolved through discussions among the reviewers. Data extraction and synthesis were conducted to map the scope and breadth of AI utilization in emerging-disease contexts.
Results: The initial search yielded a total of 1221 articles, of which 31 met the inclusion criteria. The included studies were mainly on COVID-19, plus 1 study of avian flu and 1 study of COVID-19–related mucormycosis. The AI models/tools used across various stages of disease management were machine learning, natural language processing, multilevel perception, and real-time prediction model using neural networks. The AI tools were used for early detection, prediction modeling, diagnosis, and screening and surveillance of EIDs. The use of AI technologies in EID is promising, as it aids frontline physicians by predicting minor symptom severity, reducing reliance on scarce laboratory tests during EID crises; enhances knowledge; and improves decision-making, thus improving patient care.
Conclusion: AI holds significant potential in revolutionizing the approach to EIDs by facilitating early detection, prediction, and response. This advancement will benefit healthcare professionals in managing the diseases, thus enhancing overall disease control efforts.
Statement: Public or consumers were not involved in this review.
Objective: To explore the current available evidence of AI applications in the context of EID management
Methods:
The methodology proposed by JBI in conjunction with PRISMA-ScR framework was used to conduct the review. A 3-step search strategy was used to locate both published and unpublished studies in the English language. The PubMed, Ebscohost, and Virtual Health Library were searched. Search for unpublished studies and conference proceedings was also conducted. The keywords included were “artificial intelligence, emerging diseases, health promotion, health screening, health surveillance, health prediction.”
All identified citations were collated and uploaded to Zotero. Titles and abstracts were screened by 2 independent reviewers to assess them against the inclusion and exclusion criteria using Rayyan, and duplicates were removed. Disagreements were resolved through discussions among the reviewers. Data extraction and synthesis were conducted to map the scope and breadth of AI utilization in emerging-disease contexts.
Results: The initial search yielded a total of 1221 articles, of which 31 met the inclusion criteria. The included studies were mainly on COVID-19, plus 1 study of avian flu and 1 study of COVID-19–related mucormycosis. The AI models/tools used across various stages of disease management were machine learning, natural language processing, multilevel perception, and real-time prediction model using neural networks. The AI tools were used for early detection, prediction modeling, diagnosis, and screening and surveillance of EIDs. The use of AI technologies in EID is promising, as it aids frontline physicians by predicting minor symptom severity, reducing reliance on scarce laboratory tests during EID crises; enhances knowledge; and improves decision-making, thus improving patient care.
Conclusion: AI holds significant potential in revolutionizing the approach to EIDs by facilitating early detection, prediction, and response. This advancement will benefit healthcare professionals in managing the diseases, thus enhancing overall disease control efforts.
Statement: Public or consumers were not involved in this review.