ReviewCureus2023
Machine Learning Applications in Spine Surgery.
Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed.
- Large Language Models in Spine Surgery : A Narrative Review of Performance Paradox and Clinical Integration Challenges.Journal of Korean Neurosurgical Society · 2026Article
- AI parameters for enhancing spine surgery outcomes: a narrative review.Journal of spine surgery (Hong Kong) · 2026Review
- Risk prediction of non-small cell lung cancer in patients with pulmonary nodules: a single-center cohort study based on six machine learning algorithms.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Enhancing AI Readiness in Pediatric Surgery: Impact of a Targeted Workshop on Knowledge and Competencies.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026Article
- Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.Journal of clinical medicine · 2026Review
- Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods.Health information science and systems · 2025Article
- Review
- Artificial Intelligence in Planning for Spine Surgery.Current reviews in musculoskeletal medicine · 2025Review
- Prediction of primary admission total charges following lumbar disc arthroplasty utilizing machine learning.Journal of spine surgery (Hong Kong) · 2025Article
- Utility of Enabling Technologies in Spinal Deformity Surgery: Optimizing Surgical Planning and Intraoperative Execution to Maximize Patient Outcomes.Journal of clinical medicine · 2025Review
- A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization.Diagnostics (Basel, Switzerland) · 2025Article
- Health Care Social Robots in the Age of Generative AI: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review.Brain & spine · 2025Review
- Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.Spine deformity · 2025Article
- ChatGPT's Performance in Spinal Metastasis Cases-Can We Discuss Our Complex Cases with ChatGPT?Journal of clinical medicine · 2024Article
- Artificial Intelligence and Machine Learning in Spine Research: A New Frontier.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives.Healthcare (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This literature review sought to identify and evaluate the current applications of artificial intelligence (AI)/machine learning (ML) in spine surgery that can effectively guide clinical decision-making and surgical planning. By using specific keywords to maximize search sensitivity, a thorough literature research was conducted in several online databases: Scopus, PubMed, and Google Scholar, and the findings were filtered according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 46 studies met the requirements and were included in this review. According to this study, AI/ML models were sufficiently accurate with a mean overall value of 74.9%, and performed best at preoperative patient selection, cost prediction, and length of stay. Performance was also good at predicting functional outcomes and postoperative mortality. Regression analysis was the most frequently utilized application whereas deep learning/artificial neural networks had the highest sensitivity score (81.5%). Despite the relatively brief history of engagement with AI/ML, as evidenced by the fact that 77.5% of studies were published after 2018, the outcomes have been promising. In light of the Big Data era, the increasing prevalence of National Registries, and the wide-ranging applications of AI, such as exemplified by ChatGPT (OpenAI, San Francisco, California), it is highly likely that the field of spine surgery will gradually adopt and integrate AI/ML into its clinical practices. Consequently, it is of great significance for spine surgeons to acquaint themselves with the fundamental principles of AI/ML, as these technologies hold the potential for substantial improvements in overall patient care.
Indexed as
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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.