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It is well established that repeat imaging scans for the same patient at multiple facilities, along with pricing differences in imaging services, leads to inefficient healthcare spending, not to mention patient frustration in an era of healthcare consumerization.
FREMONT, CA: As the need for radiological research grows worldwide, redundant storage requirements necessitate a large amount of storage capacity. While cloud storage systems can help with this, it's not just about storage; it's also about retrieving those photos when they're needed for future interpretations in a secure manner.
Moving huge image files from one location to another presents certain technical hurdles, but rigorous patient privacy rules add to the difficulty by inducing bureaucracy in healthcare systems to allow access to data. As a cherry on top, the healthcare business as a whole is a prime target for hackers looking to obtain access to information, making cybersecurity a top priority for custodians of such records.
Because of these obstacles, imaging centers with vast amounts of medical imaging data cannot effectively exploit or monetize the data to promote the development of artificial intelligence (AI) algorithms.
They would greatly profit from the additional revenue source due to increasing cost pressures in a value-based environment. Furthermore, a global shortage of radiologists means a lack of access or longer wait times for radiologists, resulting in lengthier turnaround times even for physicians making diagnosis. Teleradiology is a viable option for this, although access to patient imaging scans continues to be a barrier in this profession.
The same issues stymie AI algorithms in the medical imaging arena, which must be taught on current imaging images before they can "learn." Thus, while AI has the potential to alleviate some of the current difficulties in radiology, deep learning and machine learning systems rely on more diverse data to increase their capabilities. In other words, without accessible access, AI development will be stifled, and problems like transfer learning will endure.
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