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.
Highlights
Specialty section: This article was submitted to Applied Neuroimaging, a section of the journal Frontiers in Neurology
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
To help better understand the brain pathologies of patients, radiologists provide qualitative and quantitative exam results based on well-established methods in cross-sectional neuroimaging such as magnetic resonance imaging (MRI) and computed tomography (CT); we must integrate new advancements in computer technology to further evolve patient care as summarized by Kassubek [1]
Summary
To help better understand the brain pathologies of patients, radiologists provide qualitative and quantitative exam results based on well-established methods in cross-sectional neuroimaging such as magnetic resonance imaging (MRI) and computed tomography (CT); we must integrate new advancements in computer technology to further evolve patient care as summarized by Kassubek [1]. This step may seem logical, yet the introduction of the first hand-held scientific calculator in 1972 was met with apprehension; engineers would supposedly lose their basic tools of the trade [2]. Automated detection algorithms based on AI could reduce the workload; benefits of AI in radiology have already been shown in the identification and classification of microcalcification in mammography [14] and has been tested against human second readings for the purposes of breast cancer screening [15]
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