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Bridging the Gap

Machine Learning and Teledermatology to Improve Eczema Diagnosis and Treatment across Skin Colors and Ethnic Groups. This is a Literature Review of 70 research papers!

Bridging the Gap


Eczema, a chronic inflammatory skin disorder, disproportionately affects people of different races and ethnicities. To enhance patient outcomes and quality of life, accurate diagnosis and treatment are required. This review of the literature looks at the potential for machine learning and teledermatology to improve eczema diagnosis and treatment across diverse skin colors and ethnic groups, with an emphasis on improving access to care for black populations. We offer an overview of machine learning applications in dermatology, the current state of eczema diagnosis and treatment, and possible synergies in eczema care between machine learning and teledermatology. We also discuss the issues of skin color and ethnicity bias in machine learning models, as well as the efficacy of teledermatology in the diagnosis and treatment of eczema. Our findings demonstrate that machine learning has the potential to improve eczema diagnosis and treatment prediction, but further study is required to overcome biases in existing models. Teledermatology has proven successful in extending access to care, but its application in minority communities continues to confront challenges. Integrating machine learning-enhanced teledermatology might be a potential method to close the eczema treatment gap between different skin colors and ethnic groups. We identify research gaps and make recommendations for future studies to develop this multidisciplinary subject.

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