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Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review.
Mehdi Gheisari1,2, Mustafa Ghaderzadeh3, Huxiong Li1
1Institute of Artificial Intelligence, Shaoxing University, Shaoxing, China.
JMIR Mhealth and Uhealth
|January 17, 2024
Summary
Mobile apps aided COVID-19 diagnosis by analyzing various data types, with AI and deep learning showing promise for future pandemic prevention and rapid case identification.
Area of Science:
- Digital Health
- Epidemiology
- Artificial Intelligence in Medicine
Background:
- Mobile applications are crucial for modern advancements, including pandemic control.
- While mobile apps for COVID-19 detection and diagnosis have been studied, a comprehensive analysis of their role in pandemic prevention was lacking.
Approach:
- A systematic review of 535 studies from major databases was conducted.
- 42 studies met inclusion criteria for analyzing mobile app usage in COVID-19 diagnosis and detection.
- The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol guided the study selection.
Key Points:
- Mobile apps were categorized into 6 areas: contact tracing, data gathering, visualization, AI-based diagnosis, rule-based diagnosis, and data transformation.
- AI methods, particularly deep learning techniques like convolutional neural networks, showed superior performance in processing health data for COVID-19 diagnosis.
- Clinical, geographic, demographic, radiological, serological, and laboratory data were utilized by mobile apps for patient identification.
Conclusions:
- Mobile apps are powerful tools for epidemic data collection, analysis, and early detection of suspected cases.
- Integration with IoT, cloud computing, and 5G technology will enhance mobile app capabilities for future pandemics.
- Rapid diagnosis via mobile apps, utilizing diverse data and AI, can significantly improve disease management and patient treatment outcomes.
