Evidence map›Paper›PMID 41089209›Full record

SynthesisFrontiers in artificial intelligence2025

The implementation of artificial intelligence in upper extremity surgery: a systematic review.

Dylan Parry, Brennon Henderson, Paul Gaschen, Diane Ghanem, Evan Hernandez, Anceslo Idicula, Tammam Hanna, Brendan MacKay

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Dylan ParrySchool of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, United States.
Brennon HendersonSchool of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, United States.
Paul GaschenDepartment of Orthopaedic Surgery, Texas Tech University Health Sciences Center, Lubbock, TX, United States.
Diane GhanemDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, Baltimore, MD, United States.
Evan HernandezDepartment of Orthopaedic Surgery, Texas Tech University Health Sciences Center, Lubbock, TX, United States.
Anceslo IdiculaDepartment of Orthopaedic Surgery, Texas Tech University Health Sciences Center, Lubbock, TX, United States.
Tammam HannaDepartment of Orthopaedic Surgery, Texas Tech University Health Sciences Center, Lubbock, TX, United States.
Brendan MacKayDepartment of Orthopaedic Surgery, Texas Tech University Health Sciences Center, Lubbock, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The rapid expansion of artificial intelligence (AI) in medicine has led to its increasing integration into upper extremity (UE) orthopedics. The purpose of this systematic review is to investigate the current landscape and impact of AI in the field of UE surgery. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic search of PubMed was conducted to identify studies incorporating AI in UE surgery. Review articles, letters to the editor, and studies unrelated to AI applications in UE surgery were excluded. Results: After applying inclusion/exclusion criteria, 118 articles were included. The publication years ranged from 2009 to 2024, with a median and mode of 2022 and 2023, respectively. The studies were categorized into six main applications: automated image analysis (36%), surgical outcome prediction (20%), measurement tools (14%), prosthetic limb applications (14%), intraoperative aid (10%), and clinical decision support tools (6%). Discussion: AI is predominantly utilized in image analysis, including radiograph and MRI interpretation, often matching or surpassing clinician accuracy and efficiency. Additionally, AI-powered tools enhance the measurement of range of motion, critical shoulder angles, grip strength, and hand posture, aiding in patient assessment and treatment planning. Surgeons are increasingly leveraging AI for predictive analytics to estimate surgical outcomes, such as infection risk, postoperative function, and procedural costs. As AI continues to evolve, its role in UE surgery is expected to expand, improving decision-making, precision, and patient care.

Indexed as

artificial intelligencemachine learningorthopedicssurgeryupper extremity

Identifiers

PMID41089209
PMCPMC12515950

What OpenQuestion holds

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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.