Evidence map›Paper›PMID 41103897›Full record

ReviewCureus2025

Artificial Intelligence in Trauma and Orthopaedic Surgery: A Comprehensive Review From Diagnosis to Rehabilitation.

Ahmed Mohamed, Alaa Elasad, Usman Fuad, Ioannis Pengas, Adham Elsayed, Prabhakar Bhamidipati, Peter Salib

Abstract readReview
In one paragraph

Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. Review
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

7 authors.

Ahmed MohamedTrauma and Orthopaedics, Royal Cornwall Hospital, Truro, GBR.
Alaa ElasadGeneral Practice, Zagazig University, Zagazig, EGY.
Usman FuadTrauma and Orthopaedics, Royal Cornwall Hospital, Truro, GBR.
Ioannis PengasTrauma and Orthopaedics, Royal Cornwall Hospital, Truro, GBR.
Adham ElsayedTrauma and Orthopaedics, Royal Cornwall Hospital, Truro, GBR.
Prabhakar BhamidipatiTrauma and Orthopaedics, Royal Cornwall Hospital, Truro, GBR.
Peter SalibEmergency Medicine, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has presented clinical maturity in healthcare applications. AI is reshaping orthopaedic practice by enhancing the speed and efficiency of clinical decision-making, surgical planning, and research workflows. AI enables clinicians to optimize the care pathway through rapid data processing, pattern recognition, and predictive modelling. This review examines the current AI applications across the entire spectrum of orthopaedic care and its contribution to patient care and resource utilization. Despite these promising developments, several barriers prevent widespread adoption, including concerns regarding algorithm transparency, data privacy, potential bias in training datasets, and implementation costs. The path forward requires the development of explainable AI systems that clinicians can trust and validate. As AI technology continues to evolve, success will depend on augmenting human judgment with machine precision to deliver optimal care for patients with musculoskeletal conditions.

Indexed as

artificial intelligencecomputer-assisted surgerydeep learningmachine learningorthopaedic surgerypredictive analyticstrauma surgery

Identifiers

PMID41103897
PMCPMC12522458

What Socratic holds

Textmetadata
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 Socratic graph.