Evidence map›Paper›PMID 39301194›Full record

ReviewJB & JS open access

The Use of Artificial Intelligence for Orthopedic Surgical Backlogs Such as the One Following the COVID-19 Pandemic: A Narrative Review.

Adam P Henderson, Paul R Van Schuyver, Kostas J Economopoulos, Joshua S Bingham, Anikar Chhabra

Abstract readReview
In one paragraph

Review in JB & JS open access. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Review
  3. Review
  4. AI classification of knee prostheses from plain radiographs and real-world applications.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2025
    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

5 authors.

Adam P HendersonMayo Clinic Alix School of Medicine, Jacksonville, Florida.ORCID https://orcid.org/0000-0003-4792-9338
Paul R Van SchuyverMayo Clinic Department of Orthopedic Surgery, Phoenix, Arizona.ORCID https://orcid.org/0009-0002-2500-1934
Kostas J EconomopoulosMayo Clinic Department of Orthopedic Surgery, Phoenix, Arizona.
Joshua S BinghamMayo Clinic Department of Orthopedic Surgery, Phoenix, Arizona.ORCID https://orcid.org/0000-0003-1540-852X
Anikar ChhabraMayo Clinic Department of Orthopedic Surgery, Phoenix, Arizona.ORCID https://orcid.org/0000-0002-4264-079X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

➤ The COVID-19 pandemic created a persistent surgical backlog in elective orthopedic surgeries. ➤ Artificial intelligence (AI) uses computer algorithms to solve problems and has potential as a powerful tool in health care. ➤ AI can help improve current and future orthopedic backlogs through enhancing surgical schedules, optimizing preoperative planning, and predicting postsurgical outcomes. ➤ AI may help manage existing waitlists and increase efficiency in orthopedic workflows.

Identifiers

PMID39301194
PMCPMC11410334

What Socratic 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 Socratic graph.