Machine Learning in Radiology: Resistance Is Futile.
“We are the Borg. Lower your shields and surrender your ships. We will add your biological and technological distinctiveness to our own. Your culture will adapt to service us. Resistance is futile.” (1)
There is a certain inevitability to the deployment of machine learning and other advanced computational methods in medicine. Especially in radiology, among the medical specialties, there is keen interest in improving the performance of diagnostic imaging in terms that affect patient outcomes. Efforts are being applied across the spectrum of medical imaging, including a focus on improving radiologist performance. To this end, once revolutionary digital technologies are now ordinary and essential for clinical practice. These include methods for image display (color digital displays), image analysis (digital image enhancements and other postprocessing), and real-time reference resources (world wide web) to aid in interpretation. The merger of these technologies readily suggests itself, which is an indication of the inevitability of advanced computing applications such as machine learning.