ArticleNorth American Spine Society journal2025
Implementation of artificial intelligence (AI) in ASD treatment.
Article in North American Spine Society journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- Artificial Intelligence in Intraoperative Imaging and Navigation for Spine Surgery: A Narrative Review.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Adult spinal deformity (ASD) surgery remains one of the most complex and complication-prone areas of spine care, with significant variability in outcomes and high complication rates. Recent advances in artificial intelligence (AI) have shown to be promising tools to address these challenges by improving planning, prediction, and personalization. This narrative review explores the role of AI across the surgical workflow for ASD, from preoperative decision-making to intraoperative execution and postoperative care. Methods: We conducted a comprehensive narrative review of current literature and technologies related to AI in ASD surgery. Focus areas included evidence synthesis, predictive analytics, automated radiographic assessment, intraoperative navigation, patient-specific implants, and digital patient engagement. We also present a representative case example of AI-assisted deformity correction to illustrate practical clinical application. Results: AI tools have demonstrated strong potential in improving accuracy and efficiency across various domains. Machine learning algorithms outperform traditional statistical models in predicting complications, length of stay, and functional outcomes. Automated radiographic platforms reliably reproduce spinal alignment measurements and support surgical planning. Personalized instrumentation has been associated with improved alignment fidelity. Lastly, Intraoperative AR/VR platforms and AI-enhanced robotics are helping to standardize execution and reduce variability. Conclusions: AI is redefining the landscape of ASD surgery through its ability to enhance decision-making, reduce variability, and enable personalized, data-driven care. While widespread adoption requires ongoing validation and integration, current evidence supports the clinical utility of AI-assisted strategies in improving alignment outcomes and surgical safety. This review highlights the growing potential of AI to serve as a cornerstone of precision spine surgery.
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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.