Real-World Implementation of Artificial Intelligence/Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems

Ghaith Habboub; Sigurd Berven; Christopher Ames; Thomas Peterson; Thomas Mroz

doi:10.14444/8506

Real-World Implementation of Artificial Intelligence/Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems

Int J Spine Surg. 2023 Jun;17(S1):S11-S17. doi: 10.14444/8506. Epub 2023 Jun 26.

Authors

Ghaith Habboub¹, Sigurd Berven², Christopher Ames³, Thomas Peterson², Thomas Mroz⁴

Affiliations

¹ Cleveland Clinic Center for Spine Health, Cleveland, OH, USA habboug@ccf.org.
² Department of Orthopedic Surgery, UCSF Medical Center, San Francisco, CA, USA.
³ Department of Neurological Surgery, University of California, San Francisco, CA, USA.
⁴ Cleveland Clinic Center for Spine Health, Cleveland, OH, USA.

PMID: 37364939
PMCID: PMC10318912
DOI: 10.14444/8506

Abstract

Decision-making in spine surgery is complex due to patients' heterogeneity and complexity of spinal pathologies and the various surgical options applied to a given pathology. Artificial intelligence/machine learning algorithms provide an opportunity to improve patient selection, surgical planning, and outcomes. The purpose of this article is to present the experience and applications of in spine surgery at 2 large academic health care systems.

Keywords: artificial intelligence; predictive modeling; spine surgery.

This manuscript is generously published free of charge by ISASS, the International Society for the Advancement of Spine Surgery. Copyright © 2023 ISASS. To see more or order reprints or permissions, see http://ijssurgery.com.