Evidence map›Paper›PMID 40870445›Full record

ReviewMedicina (Kaunas, Lithuania)2025

Artificial Intelligence and Its Impact on the Management of Lumbar Degenerative Pathology: A Narrative Review.

Alessandro Trento, Salvatore Rapisarda, Nicola Bresolin, Andrea Valenti, Enrico Giordan

Abstract readReview
In one paragraph

Review in Medicina (Kaunas, Lithuania), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Alessandro TrentoDepartment of Neuroscience, University of Verona, 37126 Verona, Italy.ORCID 0009-0002-6751-2660
Salvatore RapisardaDepartment of Neuroscience, University of Padua, 35128 Padua, Italy.
Nicola BresolinDepartment of Neuroscience, University of Padua, 35128 Padua, Italy.
Andrea ValentiDepartment of Neuroscience, University of Padua, 35128 Padua, Italy.
Enrico GiordanNeurosurgical Department, Aulss2 Marca Trevigiana, 31100 Treviso, Italy.ORCID 0000-0002-8863-116X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this narrative review, we explore the role of artificial intelligence (AI) in managing lumbar degenerative conditions, a topic that has recently garnered significant interest. The use of AI-based solutions in spine surgery is particularly appealing due to its potential applications in preoperative planning and outcome prediction. This study aims to clarify the impact of artificial intelligence models on the diagnosis and prognosis of common types of degenerative conditions: lumbar disc herniation, spinal stenosis, and eventually spinal fusion. Additionally, the study seeks to identify predictive factors for lumbar fusion surgery based on a review of the literature from the past 10 years. From the literature search, 96 articles were examined. The literature on this topic appears to be consistent, describing various models that show promising results, particularly in predicting outcomes. However, most studies adopt a retrospective approach and often lack detailed information about imaging features, intraoperative findings, and postoperative functional metrics. Additionally, the predictive performance of these models varies significantly, and few studies include external validation. The application of artificial intelligence in treating degenerative spine conditions, while valid and promising, is still in a developmental phase. However, over the last decade, there has been an exponential growth in studies related to this subject, which is beginning to pave the way for its systematic use in clinical practice.

Indexed as

Artificial IntelligenceIntervertebral Disc DegenerationLumbar VertebraeHumansIntervertebral Disc DisplacementSpinal FusionSpinal StenosisAIartificial intelligencedegenerativelumbarspinesurgery

Identifiers

PMID40870445
PMCPMC12387989

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.