How to Implement AI in the Clinical Enterprise: Opportunities and Lessons Learned
J Am Coll Radiol . 2020 Nov;17(11):1394-1397. doi: 10.1016/j.jacr.2020.09.039.
Yvonne W Lui, Krzysztof Geras, K Tobias Block, Marc Parente, Joseph Hood, Michael P Recht
Advances in research give credence to the promise that has built up in recent times around artificial intelligence (AI) applications in medical imaging. To completely realize this promise in clinical practice, significant questions still need to be answered. Several have been discussed in detail in previous publications, including scientific questions around developing the best models to solve appropriate clinical questions [ 1 ], complex regulatory issues that span multiple agencies [ 2 , 3 ], and the creation of successful business models [ 4 , 5 ]. One important topic that has received little attention up to now is the nuts and bolts of what it takes to take an AI algorithm for medical imaging from the laboratory into full-scale clinical deployment. In this article, we will address this question and look at various aspects, from internal testing to infrastructure needs and deployment challenges.
Read Full Article Here: https://doi.org/10.1016/j.jacr.2020.09.039