AIMC Topic: Radiologists

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A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over.

European journal of radiology
PURPOSE: To evaluate the opinion and assessment of radiologists, surgeons and medical students on a number of important topics regarding the future of radiology, such as artificial intelligence (AI), turf battles, teleradiology and 3D-printing.

Exploring Large-scale Public Medical Image Datasets.

Academic radiology
RATIONALE AND OBJECTIVES: Medical artificial intelligence systems are dependent on well characterized large-scale datasets. Recently released public datasets have been of great interest to the field, but pose specific challenges due to the disconnect...

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

IEEE transactions on medical imaging
We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast...

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of M...

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

Radiology
This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of M...

Essential Elements of Natural Language Processing: What the Radiologist Should Know.

Academic radiology
Natural language is ubiquitous in the workflow of medical imaging. Radiologists create and consume free text in their daily work, some of which can be amenable to enhancements through automatic processing. Recent advancements in deep learning and "ar...

Injecting and removing suspicious features in breast imaging with CycleGAN: A pilot study of automated adversarial attacks using neural networks on small images.

European journal of radiology
PURPOSE: To train a CycleGAN on downscaled versions of mammographic data to artificially inject or remove suspicious features, and to determine whether these AI-mediated attacks can be detected by radiologists.