Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities.

ABSTRACT: Examples of fully integrated machine learning models that drive clinical care are rare. Despite major advances in the development of methodologies that outperform clinical experts and growing prominence of machine learning in mainstream medical literature, major challenges remain. At Duke Health, we are in our fourth year developing, piloting, and implementing machine learning technologies in clinical care. To advance the translation of machine learning into clinical care, health system leaders must address barriers to progress and make strategic investments necessary to bring health care into a new digital age. Machine learning can improve clinical workflows in subtle ways that are distinct from how statistics has shaped medicine. However, most machine learning research occurs in siloes,

SUBMITTER: Sendak M 

PROVIDER: S-EPMC6354017 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature