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References
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Main Article Content
Exploring machine learning potentials to improve medical imaging services of children and adolescents in low-resource settings
Abstract
None
There is growing evidence of overuse, underuse, and misuse of medicine and medical imaging globally [1,2,3,4]. These trends are worse among the children and adolescent population because they present a unique individuality within the medical imaging landscape [5]. Overuse of medical imaging implies giving patients care that they do not need, underuse means failing to give patients the right care that they need while, misuse of medical imaging implies making errors that can harm people's health [6]. The idea of giving the right medical imaging service and the right radiation dose transcends the boundaries of radiation protection and good medical practice in children and adolescents [6]. Several challenges persist in the areas of providing quality medical imaging services, radiation protection, and safety for children and adolescents in low-resource settings, especially in Sub-Sahara Africa [7]…
