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

Joshua Guedalia, Michal Lipschuetz, Sarah M. Cohen, Yishai Sompolinsky, Asnat Walfisch, Eyal Sheiner, Ruslan Sergienko, Joshua Rosenbloom, Ron Unger, Simcha Yagel, Hila Hochler

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article numbere28120
JournalJournal of Medical Internet Research
Volume23
Issue number12
DOIs
StatePublished - 1 Dec 2021
Externally publishedYes

Keywords

  • AI
  • Algorithm transport
  • Artificial intelligence
  • Birth
  • Health care facilities
  • Health outcomes
  • ML
  • Machine learning
  • Neonatal
  • Pediatrics
  • Pregnancy
  • Prenatal

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