Deploying clinical machine learning? Consider the following...
Not on the map. Why?
Abstract (arXiv)
Despite the intense attention and considerable investment into clinical machine learning research, relatively few applications have been deployed at a large-scale in a real-world clinical environment. While research is important in advancing the state-of-the-art, translation is equally important in bringing these techniques and technologies into a position to ultimately impact healthcare. We believe a lack of appreciation for several considerations are a major cause for this discrepancy between expectation and reality. To better characterize a holistic perspective among researchers and practitioners, we survey several practitioners with commercial experience in developing CML for clinical deployment. Using these insights, we identify several main categories of challenges in order to better design and develop clinical machine learning applications.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 61 references that are in the map. First posted on arXiv 14 Sep 2021.
Builds on
17 of 61 references.
References · 17
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5 more
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