ReviewPatient safety in surgery2024
The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review.
Review in Patient safety in surgery, 2024. 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, 24 citations in OpenAlex.
- Sum-of-Checks: structured reasoning for surgical safety with large vision-language models.International journal of computer assisted radiology and surgery · 2026Article
- [Artificial intelligence in spinal surgery: current applications and future perspectives].Orthopadie (Heidelberg, Germany) · 2026Review
- Random forest-based prediction of early postoperative pain after pelvic organ prolapse surgery under standardized remimazolam anesthesia.BMC anesthesiology · 2026Observational
- Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.Patient safety in surgery · 2025Review
- Smart technologies and digital innovations for improving perioperative patient safety: a review.Patient safety in surgery · 2025Review
- Predicting Surgical Site Infection after Lumbar Laminectomy and Discectomy: A Cutting-edge Algorithmic Approach by Incorporating Ensembled Stacking into the Current State-of-the-art for Automated Machine Learning.Neurosurgical review · 2025Article
- Revolutionizing spine surgery with emerging AI-FEA integration.Journal of robotic surgery · 2025Review
- Machine learning applications in forecasting patient satisfaction and clinical outcomes after carpal tunnel release: a retrospective study.BMC musculoskeletal disorders · 2025Article
- From error to prevention of wrong-level spine surgery: a review.Patient safety in surgery · 2025Review
- Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors.BMC health services research · 2025Article
- Optimizing Single-Position Prone Lateral Lumbar Interbody Fusion with Exoscopic Technology: A Review of Key Innovations.Journal of clinical medicine · 2025Review
- Exploring strategies to enhance patient safety in spine surgery: a review.Patient safety in surgery · 2025Review
- Digital health: current applications, challenges, and future directions for enhancing healthcare quality and safety.Frontiers in public health · 2025Review
- Machine learning approaches for improvement of patient safety in surgery.Patient safety in surgery · 2024Article
- Predictive accuracy of machine learning models for conservative treatment failure in thoracolumbar burst fractures.BMC musculoskeletal disorders · 2024Article
- Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Assessing the predictive capability of machine learning models in determining clinical outcomes for patients with cervical spondylotic myelopathy treated with laminectomy and posterior spinal fusion.Patient safety in surgery · 2024Article
- From complexity to clarity: A perspective on personalized spine care through genetic, psychosocial, and technological advancements.Journal of craniovertebral junction & spineReview
- Examining the relationship between digital hospital level and patient safety culture: A study on healthcare workers.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.
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.