Title |
The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading
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Published in |
Frontiers in Neurology, September 2017
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DOI | 10.3389/fneur.2017.00489 |
Pubmed ID | |
Authors |
Nicolin Hainc, Christian Federau, Bram Stieltjes, Maria Blatow, Andrea Bink, Christoph Stippich |
Abstract |
Radiologists are among the first physicians to be directly affected by advances in computer technology. Computers are already capable of analyzing medical imaging data, and with decades worth of digital information available for training, will an artificial intelligence (AI) one day signal the end of the human radiologist? With the ever increasing work load combined with the looming doctor shortage, radiologists will be pushed far beyond their current estimated 3 s allotted time-of-analysis per image; an AI with super-human capabilities might seem like a logical replacement. We feel, however, that AI will lead to an augmentation rather than a replacement of the radiologist. The AI will be relied upon to handle the tedious, time-consuming tasks of detecting and segmenting outliers while possibly generating new, unanticipated results that can then be used as sources of medical discovery. This will affect not only radiologists but all physicians and also researchers dealing with medical imaging. Therefore, we must embrace future technology and collaborate interdisciplinary to spearhead the next revolution in medicine. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Switzerland | 2 | 18% |
United Kingdom | 2 | 18% |
United States | 1 | 9% |
Canada | 1 | 9% |
Hungary | 1 | 9% |
Germany | 1 | 9% |
Unknown | 3 | 27% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 55% |
Practitioners (doctors, other healthcare professionals) | 5 | 45% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 84 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 14 | 17% |
Researcher | 12 | 14% |
Student > Ph. D. Student | 9 | 11% |
Student > Master | 8 | 10% |
Lecturer | 6 | 7% |
Other | 15 | 18% |
Unknown | 20 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 17 | 20% |
Computer Science | 12 | 14% |
Engineering | 7 | 8% |
Biochemistry, Genetics and Molecular Biology | 4 | 5% |
Neuroscience | 3 | 4% |
Other | 18 | 21% |
Unknown | 23 | 27% |