ArticleNeurospine2024
Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.
Article in Neurospine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Large Language Models in Spine Surgery : A Narrative Review of Performance Paradox and Clinical Integration Challenges.Journal of Korean Neurosurgical Society · 2026Article
- Artificial intelligence in lumbar radiography: bridging deep learning and clinical practice in low-resource environments.BMJ health & care informatics · 2026Review
- Domain-Adapted Foundation Models for Single-Click Surgical Instrument Segmentation in Spinal Endoscopy.Neurospine · 2026Article
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Bone Fusion in the Cervical Spine: Where Are We Now?Bioengineering (Basel, Switzerland) · 2026Review
- Application of a Smart Orthosis in the Treatment of Idiopathic Scoliosis-A Pilot Case Study.Sensors (Basel, Switzerland) · 2026Article
- Digital twins and multimodal artificial intelligence in spine care: a scoping review of concepts, evidence, and translational barriers.Spine deformity · 2026Review
- AI-Assisted Fracture Detection in Orthopedic and Trauma Imaging: Where It Works, Where It Fails, and Principles for Safe Clinical Deployment.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Review
- MRI evaluation of the lumbar spine: a survey-based assessment of protocols and practice patterns used by musculoskeletal radiologists in the United States.Skeletal radiology · 2026Article
- Modic changes and their role in vertebrogenic back pain: a literature review.Skeletal radiology · 2026Review
- Advancing medical imaging diagnostics using deep learning for accurate spinal disorder classification.Journal of education and health promotion · 2026Article
- Article
- CT Evaluation of Lumbar Interbody Fusion: A Comprehensive Review with an Integrated Framework for Principle-Based Interpretation.Diagnostics (Basel, Switzerland) · 2026Review
- Review
- The future is now: How AI is reshaping spine care.North American Spine Society journal · 2025Article
- Effectiveness and reliability of AI in diagnosis and robot-assisted spinal and cranial surgery: efficient outcomes and ethical worries.Annals of medicine and surgery (2012) · 2025Review
- Artificial Intelligence in Spine Surgery: Imaging-Based Applications for Diagnosis and Surgical Techniques.Current reviews in musculoskeletal medicine · 2025Review
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
Abstract
Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.
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.