Jawed AM, Zhang L, Zhang Z, Liu Q, Ahmed W, Wang H. Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation. World J Orthop 2025; 16(8): 107064 [DOI: 10.5312/wjo.v16.i8.107064]
Corresponding Author of This Article
Huan Wang, PhD, Professor, Department of Orthopaedic Surgery, Shengjing Hospital, China Medical University, No. 36 Sanhao Street, Shenyang 110004, Liaoning Province, China. spinewh@qq.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Orthop. Aug 18, 2025; 16(8): 107064 Published online Aug 18, 2025. doi: 10.5312/wjo.v16.i8.107064
Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation
Aqil M Jawed, Lei Zhang, Zhang Zhang, Qi Liu, Waqas Ahmed, Huan Wang
Aqil M Jawed, Lei Zhang, Zhang Zhang, Qi Liu, Huan Wang, Department of Orthopaedic Surgery, Shengjing Hospital, China Medical University, Shenyang 110004, Liaoning Province, China
Waqas Ahmed, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
Author contributions: Jawed AM conceived, designed, and wrote the manuscript; Zhang L, Zhang Z, Liu Q, and Ahmed W provided critical revision and helped in the analysis of the manuscript; Wang H supervised and contributed to the discussion of ideas, assisted in the correction, and proofread the manuscript; and all authors have read and agreed to the published version of the manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Huan Wang, PhD, Professor, Department of Orthopaedic Surgery, Shengjing Hospital, China Medical University, No. 36 Sanhao Street, Shenyang 110004, Liaoning Province, China. spinewh@qq.com
Received: March 14, 2025 Revised: May 2, 2025 Accepted: July 3, 2025 Published online: August 18, 2025 Processing time: 147 Days and 6.8 Hours
Abstract
Artificial intelligence (AI) and machine learning (ML) are transforming spine care by addressing diagnostics, treatment planning, and rehabilitation challenges. This study highlights advancements in precision medicine for spinal pathologies, leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms, enabling faster and more accurate detection of abnormalities. AI-powered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries. Wearable devices and virtual platforms, designed with AI, offer personalized, adaptive therapies that improve treatment adherence and recovery outcomes. AI also enables preventive interventions by assessing spine condition risks early. Despite progress, challenges remain, including limited healthcare datasets, algorithmic biases, ethical concerns, and integration into existing systems. Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care. Future developments include multimodal AI systems integrating imaging, clinical, and genetic data for holistic treatment approaches. AI and ML promise significant improvements in diagnostic accuracy, treatment personalization, service accessibility, and cost efficiency, paving the way for more streamlined and effective spine care, ultimately enhancing patient outcomes.
Core Tip: Artificial intelligence and machine learning are transforming spine care by improving diagnostics, surgical precision, and personalized rehabilitation. These technologies enable early risk assessment, accurate implant placement, and adaptive therapies, though challenges like data limitations and ethical concerns remain. Future advancements in multi-modal artificial intelligence systems promise enhanced precision, accessibility, and cost efficiency, revolutionizing patient outcomes in spine care.
