Evidence map›Paper›PMID 42834335›Full record

ReviewSpine deformity2026

Algorithmic fairness reporting in artificial intelligence for spinal deformity: a systematic review.

Ritvik R Jillala, Shivam Singh, Vikas N Vattipally, Daniel Lubelski, Amit Jain, Sang H Lee, Ali Bydon, Khaled M Kebaish, Christopher P Ames, Tej D Azad

Abstract readReview
PubMed Publisher
In one paragraph

Review in Spine deformity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Ritvik R JillalaDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
Shivam SinghDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
Vikas N VattipallyDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
Daniel LubelskiDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
Amit JainDepartment of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
Sang H LeeDepartment of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
Ali BydonDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
Khaled M KebaishDepartment of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
Christopher P AmesDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, 94143, USA.
Tej D AzadDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA. tazad1@jhmi.edu.ORCID http://orcid.org/0000-0001-7823-4294

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeArtificial intelligence (AI) and machine learning (ML) are increasingly studied for diagnosis, surgical planning, risk stratification, and outcome prediction in spinal deformity. However, these models may reproduce inequities when datasets, outcome definitions, or validation strategies incompletely represent diverse populations. Given the rapid growth of AI in spinal deformity, there is a need to evaluate how algorithmic fairness is reported and assessed.

methodsA systematic review was performed in accordance with PRISMA guidelines. PubMed, Embase, Cochrane, Scopus, and Web of Science were searched in May 2026 for studies applying AI or ML to spinal deformity care. Studies were evaluated for application domain, population, data modality, model type, validation strategy, demographic reporting, subgroup performance, calibration, and fairness audit or mitigation.

resultsFifty-eight studies were included: 33 image-based diagnosis or measurement studies (57%) and 25 clinical prediction or decision-support studies (43%). Age and sex or gender were commonly reported but rarely used for subgroup evaluation. Race, ethnicity, or skin tone were reported in 8 studies (14%), and socioeconomic or insurance-related variables in 1 study (2%). Fairness-aware evaluation was uncommon: 2 studies (3%) reported subgroup performance analyses, none evaluated subgroup calibration, and 2 studies (3%) described formal fairness audit or mitigation. Despite increasing technical sophistication, evaluation of demographic representativeness, subgroup reliability, and algorithmic equity remained limited.

conclusionsAI applications in spinal deformity show promise across diagnosis, measurement, surgical planning, prognosis, and postoperative prediction. Before clinical implementation, studies should improve demographic reporting, test subgroup performance and calibration, and evaluate whether model outputs perform equitably across clinically relevant populations.

Indexed as

Algorithmic fairnessArtificial intelligenceDiagnosisHealth equityMachine learningPredictive modelingSpinal deformitySurgical planning

Identifiers

PMID42834335

What OpenQuestion holds

Textmetadata
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.