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Turlip et al. Art Int Surg 2024;4:324-30 Artificial
DOI: 10.20517/ais.2024.29
Intelligence Surgery

Perspective Open Access

Redefining precision: the current and future roles of
artificial intelligence in spine surgery

Ryan W. Turlip, Harmon S. Khela, Mert Marcel Dagli, Daksh Chauhan, Yohannes Ghenbot, Hasan S.
Ahmad, Jang W. Yoon
Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Correspondence to: Dr. Jang W. Yoon, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania,
800 Spruce Street, Philadelphia, PA, 19107 USA. E-mail: jang.yoon@pennmedicine.upenn.edu

How to cite this article: Turlip RW, Khela HS, Dagli MM, Chauhan D, Ghenbot Y, Ahmad HS, Yoon JW. Redefining precision: the
current and future roles of artificial intelligence in spine surgery. Art Int Surg 2024;4:324-30. https://dx.doi.org/10.20517/ais.
2024.29
Received: 17 May 2024 First Decision: 23 Sep 2024 Revised: 2 Oct 2024 Accepted: 15 Oct 2024 Published: 24 Oct 2024

Academic Editor: Rafael D. de la Garza-Ramos Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
The integration of artificial intelligence (AI) into spine surgery presents a transformative approach to preoperative
and postoperative care paradigms. This paper explores the application of AI within spine surgery, focusing on
diagnostic and predictive applications. AI-driven analysis of radiographic images, facilitated by machine learning
(ML) algorithms such as convolutional neural networks (CNNs), potentially promises enhanced accuracy in
identifying spinal pathologies and deformities; by combining these techniques with patient-specific data, predictive
modeling can guide and inform diagnosis, prognosis, surgery selection, and treatment. Postoperatively, AI can
leverage data from digital wearable technology to enhance the quantity and quality of outcome measures surgeons
use to define and understand treatment success or failure. Still, challenges such as internal and external validation
of AI models remain pertinent. Future directions include incorporating continuous variables from digital biomarkers
and standardizing reporting metrics for AI studies in spine surgery. As AI continues to evolve, transparent
validation frameworks and adherence to reporting guidelines will be crucial for its successful integration into
clinical practice.

Keywords: Artificial intelligence, adult spinal deformity, radiographic imaging, machine learning, predictive
modeling, objective outcomes

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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