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AI application stores — the highway to adopting AI in radiology

Today, radiologists are overwhelmed by extravagant marketing messages about AI, promising to revolutionize diagnostics in medical imaging. However, most radiologists feel uncertain about how existing AI solutions can support them in their day-to-day work, how they can be integrated into existing workflows, and, in general, how to adopt AI.

Over the past few years, the discussion has moved from AI potentially replacing radiologists to how AI can become an ally rather than an enemy. It has also shifted from if we want to use AI to which applications can truly augment radiology and how they can be integrated into a streamlined and efficient workflow developed over several years of fine-tuning.

This article aims not only to clarify the kinds of tasks where radiologists are most likely to benefit from AI applications in the short term, but also to describe the main challenges of implementing AI and how so-called “application stores” will play a significant role in facilitating adoption.

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