Journal article
Rise of the Machines - Artificial Intelligence in Healthcare Epidemiology
Current infectious disease reports, Vol.27(1), 4
12/26/2024
DOI: 10.1007/s11908-024-00854-8
Abstract
Purpose of Review
This article delves into the current applications, challenges, and future directions of artificial intelligence (AI) in healthcare epidemiology, focusing on its integration into infection surveillance, prediction modelling, antimicrobial resistance, and antimicrobial stewardship.
Recent Findings
AI has the potential to transform key areas in healthcare epidemiology. AI-driven systems improve infection surveillance by automating data collection and analysis, enabling real-time monitoring and rapid identification of infection trends and outbreaks. Predictive modeling benefits from AI through processing of big data and other unconventional data sources. AI can augment antimicrobial stewardship by individualizing antimicrobial therapy, reducing inappropriate use. Finally, AI aids in addressing antimicrobial resistance by facilitating rapid detection of resistance patterns, enhancing surveillance, and accelerating the discovery of new antibiotics. Together, these advancements promise more effective management of infectious diseases and optimized healthcare interventions.
Summary
The role of AI in healthcare epidemiology is still getting defined but is showing a lot of potential in many areas. Ongoing challenges include data quality, ethical considerations, and the need for explainable AI models to ensure transparency and trust among healthcare professionals.
Details
- Title: Subtitle
- Rise of the Machines - Artificial Intelligence in Healthcare Epidemiology
- Creators
- Lemuel R Non - University of IowaAlexandre R Marra - University of IowaDilek Ince - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Current infectious disease reports, Vol.27(1), 4
- Publisher
- Springer US
- DOI
- 10.1007/s11908-024-00854-8
- ISSN
- 1523-3847
- eISSN
- 1534-3146
- Language
- English
- Date published
- 12/26/2024
- Academic Unit
- Infectious Diseases; Internal Medicine
- Record Identifier
- 9984769629602771
Metrics
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