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Embracing machine learning and digital health technology for precision dermatology
Shannon Wongvibulsin1,2, Byron Kalm-Tsun Ho3, Shawn G Kwatra3
1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
The Journal of Dermatological Treatment
|May 25, 2019
Summary
Machine learning and digital health technologies can advance precision medicine in dermatology. These tools will enhance patient engagement and clinician effectiveness, not replace dermatologists.
Area of Science:
- Digital health technologies and machine learning in dermatology.
- Precision medicine applications in skin health.
Background:
- The dermatology community can leverage current technological advancements.
- Sensors, personal health devices, computing, and machine learning are transforming healthcare.
Purpose of the Study:
- To outline a framework for integrating machine learning into dermatology.
- To highlight the potential of machine learning for precision dermatology.
Main Methods:
- Framework development for machine learning integration.
- Analysis of digital health technology potential in dermatology.
Main Results:
- Machine learning combined with digital health offers significant potential.
- Expanded access to dermatological services and improved patient management are achievable.
Conclusions:
- Machine learning is essential for realizing precision dermatology.
- Technological integration will empower clinicians and enhance patient care without replacing dermatologists.
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