ReviewFrontiers in medicine2026
The role of artificial intelligence in advancing scoliosis care: a rapid review of current evidence and future opportunities.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Does research activity reflect the population burden of scoliosis? A bibliometric analysis of etiologic subtypes.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
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
Background: Adolescent idiopathic scoliosis (AIS), is a complex three-dimensional deformity of the spine that affects a significant percentage of the adolescent population. Its progressive nature and evolution variability complicates therapeutic decisions, generating the need for more accurate tools for diagnosis, prediction of risk of progression and optimization of treatments. Artificial intelligence (AI) and machine learning (ML) emerge as tools with significant potential for comprehensive management of AIS. Despite the enthusiasm for these applications, there are important limitations that need to be addressed. The aim of this rapid review is to address a timely synthesis of available research and assess the quality of published reviews. Methods: Systematic reviews and meta-analyses published by April 2025 in English on scoliosis and any intervention involving AI were included. Search was performed in Embase, Cochrane Review Database and Pubmed/medline using the terms MesH scoliosis idiopathic and intelligence artificial and systematic reviews and meta-analysis. Two independent reviewers screened titles and abstracts following the PRISMA RR guidelines and assessed full text articles with the AMSTAR 2 tool. Any disagreement was resolved by a third reviewer. Results: Five systematic reviews met inclusion criteria. 55% of the included studies used AI algorithms with convolutional neural networks, artificial neural networks, decision trees, support vector machines, and hybrid models. The main applications in AIS were automatic Cobb angle measurement with high accuracy (< 3° in some models), curve type classification, prediction of curve progression and patient education and clinical decision support using language models. Conclusion: AI offers promising solutions for AIS management, particularly in automated Cobb angle measurement and progression prediction. Combining deep learning models with clinical data may transform future practice, but external validation and clinical integration must be strengthened to enable effective implementation.
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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.