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.

翻译：尽管临床机器学习研究备受关注且投入巨大，但能在现实临床环境中大规模部署的应用却相对较少。虽然研究对于推动技术发展至关重要，但将技术转化为能够真正影响医疗健康的工具同样重要。我们认为，对若干关键考量因素缺乏认识是导致预期与现实之间存在差距的主要原因。为了帮助研究人员和从业者建立更全面的视角，我们访谈了多位具有商业开发经验的临床机器学习从业者，总结了其在临床部署实践中遇到的挑战。基于这些洞见，我们识别出几类主要挑战，以更好地指导临床机器学习应用的设计与开发。