ArticleGlobal spine journal2022
Artificial Learning and Machine Learning Applications in Spine Surgery: A Systematic Review.
Article in Global spine journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction of proximal junctional pathology after adult spinal deformity surgery: a systematic review and diagnostic test accuracy meta-analysis.Acta neurochirurgica · 2026Pooled it
- Use of Machine Learning and Imaging to Predict Outcomes After Lumbar Spine Surgery: A Systematic Review.Journal of imaging informatics in medicine · 2026Review
- AI parameters for enhancing spine surgery outcomes: a narrative review.Journal of spine surgery (Hong Kong) · 2026Review
- Comparison of Machine Learning Algorithms for Predicting Spine Surgery Duration.Medicina (Kaunas, Lithuania) · 2026Article
- Prediction of Clinically Significant Improvement after Lumbar Fusion Surgery Based on Machine Learning.Yonsei medical journal · 2026Article
- Human Versus Machine in Survival Prediction for Metastatic Spinal Cord Compression: A Retrospective Cohort Study.Cureus · 2026Article
- Artificial intelligence in spine care: A scoping review of treatment applications.North American Spine Society journal · 2026Review
- Review
- Review
- The future of intraoperative neuromonitoring (IONM) in spinal surgery.North American Spine Society journal · 2025Article
- Benefit-Risk Reporting for FDA-Cleared Artificial Intelligence-Enabled Medical Devices.JAMA health forum · 2025Article
- Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation.World journal of orthopedics · 2025Review
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Machine Learning Models Leveraging Smartphone-Based Patient Mobility Data Can Accurately Predict Functional Outcomes After Spine Surgery.Journal of clinical medicine · 2024Article
- Development and Internal Validation of Machine Learning to Predict Postoperative Worse Functional Status after Surgical Treatment for Thoracic Spinal Stenosis.Medical science monitor : international medical journal of experimental and clinical research · 2024Article
- Large Language Model Prompting Techniques for Advancement in Clinical Medicine.Journal of clinical medicine · 2024Review
- Using Machine Learning Models to Identify Factors Associated With 30-Day Readmissions After Posterior Cervical Fusions: A Longitudinal Cohort Study.Neurospine · 2024Article
- Pushing the Limits of Minimally Invasive Spine Surgery-From Preoperative to Intraoperative to Postoperative Management.Journal of clinical medicine · 2024Review
- Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery.JAMA network open · 2024Article
- Using machine learning and big data for the prediction of venous thromboembolic events after spine surgery: A single-center retrospective analysis of multiple models on a cohort of 6869 patients.Journal of craniovertebral junction & spineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis current systematic review sought to identify and evaluate all current research-based spine surgery applications of AI/ML in optimizing preoperative patient selection, as well as predicting and managing postoperative outcomes and complications.
methodsA comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA guidelines.
resultsAfter application of inclusion and exclusion criteria, 41 studies were included in this review. Bayesian networks had the highest average AUC (.80), and neural networks had the best accuracy (83.0%), sensitivity (81.5%), and specificity (71.8%). Preoperative planning/cost prediction models (.89,82.2%) and discharge/length of stay models (.80,78.0%) each reported significantly higher average AUC and accuracy compared to readmissions/reoperation prediction models (.67,70.2%) (
conclusionsGenerally, authors of the reviewed studies concluded that AI/ML offers a potentially beneficial tool for providers to optimize patient care and improve cost-efficiency. More specifically, AI/ML models performed best, on average, when optimizing preoperative patient selection and planning and predicting costs, hospital discharge, and length of stay. However, models were not as accurate in predicting postoperative complications, adverse events, and readmissions and reoperations. An understanding of AI/ML-based applications is becoming increasingly important, particularly in spine surgery, as the volume of reported literature, technology accessibility, and clinical applications continue to rapidly expand.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.