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Articles linked to this work by shared authors, journal, and citation graph.

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Author Reply to "Regarding 'Deteriorated Quality and Media Retraction of Tendon Following Acute Traumatic Rotator Cuff Tear Are Predictors of Retear After Arthroscopic Repair'".

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2026
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Current management of impingement syndrome and repairable rotator cuff tears in South Korea: A web-based survey.

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The mid-term outcomes on reverse total shoulder arthroplasty with isolated latissimus dorsi transfer in patients with isolated external rotation lag without pseudoparalysis: an average follow-up of 5 years.

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Patch Augmentation Using Acellular Dermal Matrix During Arthroscopic Rotator Cuff Repair Reduces Retear Rates in High-Risk Patients With Rotator Cuff Healing Index 7 or Higher.

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Artificial intelligence in shoulder arthroplasty: how smart is it?

Hyun Gon Kim1, Su Cheol Kim2, Jong Hun Park2

  • 1Department of Orthopedic Surgery, Korea University Ansan Hospital, Ansan, Republic of Korea.

JSES International
|June 9, 2025
PubMed
Summary

Artificial intelligence (AI) is revolutionizing shoulder arthroplasty by predicting patient outcomes, hospital stays, and costs. AI integration enables personalized surgical care and improved clinical decision-making for surgeons.

Keywords:
Artificial intelligenceClinical decisionDeep learningMachine learningPredictionShoulder arthroplasty

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Area of Science:

  • Orthopedic Surgery
  • Medical Artificial Intelligence

Background:

  • Artificial intelligence (AI) applications are rapidly expanding across various fields, including medicine.
  • The exponential growth of surgical data presents significant opportunities for AI integration into clinical practice.

Purpose of the Study:

  • To critically analyze the current literature on AI technology in shoulder arthroplasty.
  • To understand the potential of AI in improving surgical decision-making and patient care.

Main Methods:

  • A comprehensive literature search was performed in January 2024.
  • Databases searched included PubMed (MEDLINE), SCOPUS, and EMBASE.
  • Relevant literature on AI in shoulder arthroplasty underwent critical analysis.

Main Results:

  • AI models demonstrate predictive value for hospital stay length and healthcare costs in shoulder arthroplasty.
  • AI can predict clinical outcomes and complications, aiding in implant identification.
  • Machine learning facilitates surgeons' clinical decisions, problem prediction, resource estimation, and personalized patient care.

Conclusions:

  • AI technology is advancing rapidly in medicine, offering significant potential for shoulder arthroplasty.
  • Leveraging AI allows for enhanced clinical decision-making, problem prediction, and personalized patient treatment.
  • Despite current limitations, AI's rapid advancement necessitates awareness and interest from shoulder surgeons.