2021/09/14 by Charles Lu, Lu, Charles, Ken Chang +13
Computer Science · Health Professions · Medicine · #Artificial Intelligence in Healthcare and Education #Computers and Society (cs.CY) #Electronic Health Records Systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2109.06919
openalex publication_date 2021/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.