Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation

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...

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Autores principales: Guedalia, Joshua, Lipschuetz, Michal, Cohen, Sarah M, Sompolinsky, Yishai, Walfisch, Asnat, Sheiner, Eyal, Sergienko, Ruslan, Rosenbloom, Joshua, Unger, Ron, Yagel, Simcha, Hochler, Hila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Viewpoint
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709908/
https://www.ncbi.nlm.nih.gov/pubmed/34890352
http://dx.doi.org/10.2196/28120
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author Guedalia, Joshua
Lipschuetz, Michal
Cohen, Sarah M
Sompolinsky, Yishai
Walfisch, Asnat
Sheiner, Eyal
Sergienko, Ruslan
Rosenbloom, Joshua
Unger, Ron
Yagel, Simcha
Hochler, Hila
author_facet Guedalia, Joshua
Lipschuetz, Michal
Cohen, Sarah M
Sompolinsky, Yishai
Walfisch, Asnat
Sheiner, Eyal
Sergienko, Ruslan
Rosenbloom, Joshua
Unger, Ron
Yagel, Simcha
Hochler, Hila
author_sort Guedalia, Joshua
collection PubMed
description 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.
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spelling pubmed-87099082022-01-10 Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation Guedalia, Joshua Lipschuetz, Michal Cohen, Sarah M Sompolinsky, Yishai Walfisch, Asnat Sheiner, Eyal Sergienko, Ruslan Rosenbloom, Joshua Unger, Ron Yagel, Simcha Hochler, Hila J Med Internet Res Viewpoint 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. JMIR Publications 2021-12-10 /pmc/articles/PMC8709908/ /pubmed/34890352 http://dx.doi.org/10.2196/28120 Text en ©Joshua Guedalia, Michal Lipschuetz, Sarah M Cohen, Yishai Sompolinsky, Asnat Walfisch, Eyal Sheiner, Ruslan Sergienko, Joshua Rosenbloom, Ron Unger, Simcha Yagel, Hila Hochler. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.12.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Viewpoint
Guedalia, Joshua
Lipschuetz, Michal
Cohen, Sarah M
Sompolinsky, Yishai
Walfisch, Asnat
Sheiner, Eyal
Sergienko, Ruslan
Rosenbloom, Joshua
Unger, Ron
Yagel, Simcha
Hochler, Hila
Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation
title Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation
title_full Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation
title_fullStr Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation
title_full_unstemmed Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation
title_short Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation
title_sort transporting an artificial intelligence model to predict emergency cesarean delivery: overcoming challenges posed by interfacility variation
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709908/
https://www.ncbi.nlm.nih.gov/pubmed/34890352
http://dx.doi.org/10.2196/28120
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