Artificial intelligence (AI)–based systems have been shown to reliably recognize cardiovascular disease risk1 and diagnose conditions such as diabetic retinopathy2,3 and melanoma4 from medical images. These advances in image-based medical diagnosis have been widely publicized in the media and similar tools have been approved by the US Food and Drug Administration (FDA). In April of 2018, the FDA approved the first AI device to provide screening decision for a disease (ie, diabetic retinopathy) without assisted interpretation by a clinician.5 Kanagasingam et al6 evaluated a similar approach—a convolutional neural network algorithm, a deep learning method—for identifying diabetic retinopathy from medical images in a primary care setting in Midland, Western Australia. Their system correctly classified the 2 severe cases captured in the data (193 patients with diabetes), and misclassified 15 (false-positives) individuals as having diabetic retinopathy. The number of patients needing to be reviewed by an ophthalmologist was less than 10%. These findings demonstrate the potential for these systems to support efficient and improved care, while also highlighting the need for rigorous evaluation in clinical settings.
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Nsoesie EO. Evaluating Artificial Intelligence Applications in Clinical Settings. JAMA Netw Open. 2018;1(5):e182658. doi:10.1001/jamanetworkopen.2018.2658
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