Deploying clinical machine learning? Consider the following...
This work targets researchers and practitioners in healthcare AI to improve translation of ML into clinical practice, but it is incremental as it synthesizes existing insights rather than introducing new methods.
The paper addresses the gap between clinical machine learning research and real-world deployment by surveying practitioners to identify key challenges, aiming to guide better design and development of applications.
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.