Machine Learning in Medical Emergencies: a Systematic Review and Analysis.
Inés Robles Mendo1, Gonçalo Marques1,2, Isabel de la Torre Díez3
1Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47.011, Valladolid, Spain.
Journal of Medical Systems
|August 19, 2021
Summary
Artificial intelligence (AI) in healthcare emergencies is rapidly advancing. This review analyzes AI
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Emergency Medicine
Background:
- The application of artificial intelligence (AI) in medical and emergency settings is growing, yet its specific functionalities remain unclear.
- A comprehensive understanding of AI's role in healthcare emergencies is crucial for effective implementation.
Purpose of the Study:
- To systematically review and evaluate existing research on AI applications in healthcare emergencies.
- To analyze the implementation of AI across various platforms and commercial applications.
Main Methods:
- A systematic literature search using PRISMA methodology across databases like PubMed, Scopus, Google Scholar, and IEEE Xplore.
- Review of 12 commercial mobile health applications available on Android and iOS platforms.
Main Results:
- Twenty studies were included, focusing primarily on clinical decision support (20%) and medical/emergency services (20%).
- Of the 12 tested apps, 25% focused on pre-hospital care and 25% on clinical decision support.
- Half of the evaluated apps utilized machine learning, particularly natural language processing.
Conclusions:
- Machine learning, especially natural language processing, shows significant potential in improving healthcare efficiency and quality.
- The integration of AI with mobile health devices offers promising real-time patient health assessment and management capabilities.
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