%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 23
%N 12
%P e28120
%T Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation
%A Guedalia,Joshua
%A Lipschuetz,Michal
%A Cohen,Sarah M
%A Sompolinsky,Yishai
%A Walfisch,Asnat
%A Sheiner,Eyal
%A Sergienko,Ruslan
%A Rosenbloom,Joshua
%A Unger,Ron
%A Yagel,Simcha
%A Hochler,Hila
%+ Division of Obstetrics & Gynecology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Churchill Avn. 8, Jerusalem, 9765415, Israel, 972 25841111, michal.lipschuetz@gmail.com
%K machine learning
%K algorithm transport
%K health outcomes
%K health care facilities
%K artificial intelligence
%K AI
%K ML
%K pregnancy
%K birth
%K pediatrics
%K neonatal
%K prenatal
%D 2021
%7 10.12.2021
%9 Viewpoint
%J J Med Internet Res
%G English
%X Research using artificial intelligence (AI) in medicine is expected to significantly influence the practice of medicine and the delivery of health care in the near future. However, for successful deployment, the results must be transported across health care facilities. We present a cross-facilities application of an AI model that predicts the need for an emergency caesarean during birth. The transported model showed benefit; however, there can be challenges associated with interfacility variation in reporting practices.
%M 34890352
%R 10.2196/28120
%U https://www.jmir.org/2021/12/e28120
%U https://doi.org/10.2196/28120
%U http://www.ncbi.nlm.nih.gov/pubmed/34890352