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      "text": "Future directions for such diagnostic tools such as these are still murky. In 2017, there were 235 skin cancer focused dermatology apps available on app stores (Flaten et al. 2018). In 2021, Google announced that it would be piloting its own dermatological assistant as an app, which would sit within Google search.",
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      "text": "Computer vision has yielded systems that are valuable for the clinical diagnosis of skin conditions. Dermatologists, typically first to assess the likelihood that skin lesions are malignant, look at features, such as outline, dimensions, and color. Computerized visual learning systems, trained on vast numbers of cases, have improved significantly, according to research published in Nature in 2017 (Esteva et al. 2017). In this study, researchers trained a machine learning model with a dataset of 129,450 images, each labeled as cancerous or non-cancerous.",
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      "text": "Addressing the same question about automated mole recognition systems that we did about FRI; do they raise similar concerns about machine reading of the human body? We think not. Because machine reading necessarily is probabilistic, it is important to ask whether automation serves efficiency for medical caregivers at a cost to patients' wellbeing.",
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