Evidence map›Paper›PMID 39996084›Full record

ArticleJournal of experimental orthopaedics2025

Exploring artificial intelligence in orthopaedics: A collaborative survey from the ISAKOS Young Professional Task Force.

Filippo Familiari, Adnan Saithna, Juan Pablo Martinez-Cano, Jorge Chahla, Juan Miguel Del Castillo, Nicholas N DePhillipo, Gilbert Moatshe, Edoardo Monaco, Jaime Palos Lucio, Pieter D'Hooghe and 2 more

Abstract read
In one paragraph

Article in Journal of experimental orthopaedics, 2025. 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. Artificial intelligence agents in orthopaedics: Concepts, capabilities and the road ahead.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025
    Review
  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

12 authors.

Filippo FamiliariDepartment of Orthopaedic and Trauma Surgery Magna Graecia University Catanzaro Italy.ORCID 0000-0002-3453-2043
Adnan SaithnaDepartment of Orthopedic Surgery University of Arizona Tucson Arizona USA.
Juan Pablo Martinez-CanoDepartamento de Ortopedia Fundación Valle del Lili Cali Colombia.
Jorge ChahlaDepartment of Orthopaedic Surgery Rush University Medical Center Chicago Illinois USA.
Juan Miguel Del CastilloClínica de Traumatología y Ortopedia Universidad de la República Montevideo Uruguay.
Nicholas N DePhillipoDepartment of Orthopedics University of Pennsylvania Philadelphia Pennsylvania USA.
Gilbert MoatsheOslo Sports Trauma Research Center Norwegian School of Sports Science Oslo Norway.
Edoardo MonacoOrthopaedic Unit University of Rome La Sapienza, Sant'Andrea Hospital Rome Italy.
Jaime Palos LucioDepartment of Orthopedic Surgery Hospital Central Dr Ignacio Morones Prieto San Luis Potosí Mexico.ORCID 0000-0002-2896-1984
Pieter D'HoogheAspetar Orthopaedic and Sports Medicine Hospital Doha Qatar.
Robert F LaPradeTwin Cities Orthopedics Edina Minnesota USA.
ISAKOS Young Professionals Task Force

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Through an analysis of findings from a survey about the use of artificial intelligence (AI) in orthopaedics, the aim of this study was to establish a scholarly foundation for the discourse on AI in orthopaedics and to elucidate key patterns, challenges and potential future trajectories for AI applications within the field. Methods: The International Society of Arthroscopy, Knee Surgery and Orthopaedic Sports Medicine (ISAKOS) Young Professionals Task Force developed a survey to collect feedback on issues related to the use of AI in the orthopaedic field. The survey included 26 questions. Data obtained from the completed questionnaires were transferred to a spreadsheet and then analyzed. Results: Two hundred and eleven orthopaedic surgeons completed the survey. The survey encompassed responses from a diverse cohort of orthopaedic professionals, predominantly comprising males (92.9%). There was wide representation across all geographic regions. A notable proportion (52.1%) reported uncertainty or lack of differentiation among AI, machine learning and deep learning (47.9%). Respondents identified imaging-based diagnosis (60.2%) as the primary field of orthopaedics poised to benefit from AI. A considerable proportion (25.1%) reported using AI in their practice, with primary reasons including referencing scientific literature/publications (40.3%). The vast majority expressed interest in leveraging AI technologies (95.3%), demonstrating an inclination towards incorporating AI into orthopaedic practice. Respondents indicated specific areas of interest for further study, including prediction of patient outcomes after surgery (30.8%) and image-based diagnosis of osteoarthritis (28%). Conclusions: This survey demonstrates that there is currently limited use of AI in orthopaedic practice, mainly due to a lack of knowledge about the subject, a lack of proven evidence of its real utility and high costs. These findings are in accordance with other surveys in the literature. However, there is also a high level of interest in its use in the future, in increased study and further research on the subject, so that it can be of real benefit and make AI an integral part of the orthopaedic surgeon's daily work. Level of Evidence: Level IV, survey study.

Indexed as

arthroscopyartificial intelligencediagnosisorthopaedicssports medicine

Identifiers

PMID39996084
PMCPMC11848192

What OpenQuestion 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 OpenQuestion graph.