Evidence mapPaperPMID 39886341Full record

ReviewThe archives of bone and joint surgery2025

Artificial Intelligence and the State of the Art of Orthopedic Surgery.

Mohammad Amin Khojastehnezhad, Pouya Youseflee, Ali Moradi, Mohammad H Ebrahimzadeh, Nafiseh Jirofti

Abstract readReview
In one paragraph

Review in The archives of bone and joint surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Review
  8. Review
  9. AI classification of knee prostheses from plain radiographs and real-world applications.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2025
    Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. 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.

Mohammad Amin KhojastehnezhadOrthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Sciences, Mashhad, Iran.
Pouya YousefleeOrthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Sciences, Mashhad, Iran.
Ali MoradiOrthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Sciences, Mashhad, Iran.
Mohammad H EbrahimzadehOrthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Sciences, Mashhad, Iran.
Nafiseh JiroftiOrthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Sciences, Mashhad, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) is rapidly transforming healthcare, particularly in orthopedics, by enhancing diagnostic accuracy, surgical planning, and personalized treatment. This review explores current applications of AI in orthopedics, focusing on its contributions to diagnostics and surgical procedures. Key methodologies such as artificial neural networks (ANNs), convolutional neural networks (CNNs), support vector machines (SVMs), and ensemble learning have significantly improved diagnostic precision and patient care. For instance, CNN-based models excel in tasks like fracture detection and osteoarthritis grading, achieving high sensitivity and specificity. In surgical contexts, AI enhances procedures through robotic assistance and optimized preoperative planning, aiding in prosthetic sizing and minimizing complications. Additionally, predictive analytics during postoperative care enable tailored rehabilitation programs that improve recovery times. Despite these advancements, challenges such as data standardization and algorithm transparency hinder widespread adoption. Addressing these issues is crucial for maximizing AI's potential in orthopedic practice. This review emphasizes the synergistic relationship between AI and clinical expertise, highlighting opportunities to enhance diagnostics and streamline surgical procedures, ultimately driving patient-centric care.

Indexed as

Artificial intelligenceDiagnostic imagingMachine learningOrthopedicsPersonalized treatmentPredictive modelingRobotic surgery

Identifiers

PMID39886341
PMCPMC11776378

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.