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

Aug 5, 2026

Spine surgery decision-making is challenging because patient presentations are heterogeneous and spinal pathologies and surgical options vary widely. Artificial intelligence and machine learning can support better patient selection, surgical planning, and outcome improvement through analytic methods that capture complex variable relationships beyond conventional multivariate approaches. This article shares real-world experience and applications of AI/ML in spine surgery across two large academic health care systems, while addressing limitations such as reporting quality, clinician workload, fairness, privacy, explainability, and interpretability.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

Background

Clinical complexity and heterogeneity in spine care

AI/ML capabilities and key limitations

AI/ML use cases across surgical spine workflows

Levels of AI/ML automation and safety considerations

UCSF UC San Francisco Previously Published Works Title Real-World Implementation of Artificial Intelligence/Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems. Permalink [https://escholarship.org/uc/item/1hg7n2vb](https://escholarship.org/uc/item/1hg7n2vb) Journal The International Journal of Spine Surgery, 17(S1) ISSN 2211-4599 Authors Habboub, Ghaith Berven, Sigurd Ames, Christopher et al. Publication Date 2023-06-01 DOI 10.14444/8506 Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California International Journal of Spine Surgery, Vol. 17, No. S1, 2023, pp. S11–S17 [https://doi.org/10.14444/8506](https://doi.org/10.14444/8506) © International Society for the Advancement of Spine Surgery Real-World Implementation of Artificial Intelligence/ Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems GHAITH HABBOUB, MD1 ; SIGURD BERVEN, MD2 ; CHRISTOPHER AMES, MD3 ; THOMAS PETERSON, PhD2 ; AND THOMAS MROZ, MD1 1Cleveland Clinic Center for Spine Health, Cleveland, OH, USA; 2Department of Orthopedic Surgery, UCSF Medical Center, San Francisco, CA, USA; 3Department of Neurological Surgery, University of California, San Francisco, CA, USA 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. Special Issue Keywords: artificial intelligence, predictive modeling, spine surgery BACKGROUND Surgical decision-making in spine surgery is complex due to heterogeneity in patients, pathologies, and surgical options. Spine patients may present with significant variability in self-reported health status, comorbidities, and surgical approaches to care.1–6 The heterogeneity of patient presentations and surgical approaches has important implications for the cost and outcomes of care.7 Patient-collected data are growing in both size and complexity. Consequently, clinicians are limited in their ability to consider all patient data in decision-making regarding risk and outcome assessment. Precision medicine approaches to care may empower patients and physicians to make informed decisions regarding appropriate care and hold promise to optimize outcomes of care.7 Artificial intelligence/machine learning (AI/ML) are valuable analytic techniques for the development of models to help personalize decision-making. AI/ML are tools that provide correlations between variables that may not be apparent in traditional multivariate analysis and may reduce the biases of hypothesis-driven modeling.8 In contrast, AI/ML techniques are limited by important issues including poor reporting methodologies, increased clinician burden, fairness, privacy/ anonymity, explainability, and interpretability.6,9,10 Particularly for the medical domain, interpretability is critical in providing trust between the AI/ML algorithm and the health care system.11 Although similar algorithms with similar accuracy can behave differently in deployed settings, global and local interpretability methods can help in model selection and generalization. 12,13 It is crucial to think about AI/ML algorithms as devices with end users and process flow. These devices require documentation, regulations, and maintenance. 14,15 The regulation of ML algorithms is evolving given limited precedence. AI/ML has been used across multiple aspects of spine surgery16 including spinal medical imaging,17,18 predicting surgical outcome,19–22 identifying patients’ characteristics in deformity patients,23,24 enhancing robotics,25,26 for

FAQs

Why is decision-making in spine surgery difficult?

Because patients vary substantially in health status, comorbidities, and spinal pathologies, and multiple surgical options may apply to the same condition.

How can AI/ML improve surgical spine care?

AI/ML can enhance patient selection, surgical planning, and outcomes by modeling relationships between variables that may not be captured by traditional multivariate analysis.

What limitations must be considered when deploying AI/ML in medicine?

Key concerns include poor reporting methods, increased clinician burden, fairness, privacy/anonymity, and the need for explainability and interpretability for clinical trust.

Aug 5, 2026

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