Evidence map›Paper›PMID 35478980›Full record

ReviewSpine surgery and related research2022

Quantum Computing: The Future of Big Data and Artificial Intelligence in Spine.

G Michael Mallow, Alexander Hornung, Juan Nicolas Barajas, Samuel S Rudisill, Howard S An, Dino Samartzis

Abstract readReview
In one paragraph

Review in Spine surgery and related research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
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

6 authors.

G Michael MallowDepartment of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center, Chicago, USA.
Alexander HornungDepartment of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center, Chicago, USA.
Juan Nicolas BarajasDepartment of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center, Chicago, USA.
Samuel S RudisillDepartment of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center, Chicago, USA.
Howard S AnDepartment of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center, Chicago, USA.
Dino SamartzisDepartment of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center, Chicago, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the emergence of big data and more personalized approaches to spine care and predictive modeling, data science and deep analytics are taking center-stage. Although current techniques in machine learning and artificial intelligence have gained attention, their applications remain limited by their reliance on traditional analytic platforms. Quantum computing has the ability to circumvent such constraints by attending to the various complexities of big data while minimizing space and time dimensions within computational algorithms. In doing so, quantum computing may one day address research and clinical objectives that currently cannot be tackled. Understanding quantum computing and its potential to improve patient management and outcomes is therefore imperative to drive further advancements in the spine field for the next several decades.

Indexed as

artificial intelligencebig datacomputingdisc degenerationpainquantumspine

Identifiers

PMID35478980
PMCPMC8995124

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.