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In spite of the promise it holds and the anticipation it has generated, artificial intelligence (AI) in medicine is not immune to bottlenecks translating the technology from research to clinical practice. Medical professionals are often not actively engaged in developing these AI-based applications and tools. On the other hand, industrial experts submerged in the effort lack relevant knowledge and deep understanding of the challenges faced by clinical staff. As such, there remains a significant gap between cutting-edge innovation and what goes on in real life.

Furthermore, the large datasets required to efficiently train AI algorithms are hard to come by. They are even more difficult to curate properly without the direct input of medical professionals who realize the importance of these technologies. Coupled with the lack of infrastructure to continuously validate and scale up the developed algorithms, translating AI into clinical practice remains near yet so far.

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Radiology Partners
VP of Clinical Operations, Radiology Partners
Global Enterprise Imaging Principal
Pure Storage


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