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      "text": "A 2020 analysis of over 60 recently FDA approved artificial intelligence- and machine learning-based medical devices and algorithms indicates that they are predominantly geared at fields of radiology (46.9%), cardiology (25.0%), and internal/general medical practice (15.6%) but many have cross-disciplinary functionalities. AI/ML technologies have the potential to diagnose, manage and treat a wide range of medical conditions; they can help assess and interpret X-ray and magnetic resonance images, improve workflow and thus reduce waiting times, support medication adherence, customize insulin dosages, and more. Although highly promising, there are many obstacles to the implementation of AI/ML particularly in regulatory areas as well as everyday clinical practice. Issues include software transparency, data bias and safety",
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      "text": "Cardiology; Neurology; Endocrinology; Internal medicine; Radiology; Ophtalmology; Emergency medicine; Oncology",
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      "text": "Diagnosis of sleep disorders; Detection of diabetic retinopathy; Stroke detection on CT; Predicting blood glucose changes; Breast density via mammography; ECG analysis support; Radiology image processing software; Measure liver iron concentration; Mammogram workflow; Chest x-ray assessment pneumothorax; Quantification and reporting of results of cardiovascular function; Radiological software for lesions suspicious for cancer; Cardiac monitor",
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      "text": "Selected FDA approvals for artificial intelligence (AI) and machine learning (ML)-based devices in medicine, 2017 onwards",
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